Distributing a crowdsourced sparse map for autonomous vehicle navigation

ABSTRACT

Systems and methods are provided for distributing a crowdsourced sparse map for autonomous vehicle navigation. In one implementation, a method of generating a road navigation model for use in autonomous vehicle navigation may include receiving navigation information associated with a common road segment from a plurality of vehicles; storing the navigation information associated with the common road segment; generating at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information; and distributing the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating along the common road segment. The autonomous vehicle road navigation model may include at least one line representation of a road surface feature extending along the common road segment, and each line representation may representing a path along the common road segment substantially corresponding with the road surface feature.

CROSS REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalPatent Application No. 62/365,192, filed on Jul. 21, 2016, and U.S.Provisional Patent Application No. 62/373,153, filed on Aug. 10, 2016.All of the foregoing applications are incorporated herein by referencein their entirety.

BACKGROUND Technical Field

The present disclosure relates generally to autonomous vehiclenavigation and a sparse map for autonomous vehicle navigation. Inparticular, this disclosure relates to systems and methods forcrowdsourcing a sparse map for autonomous vehicle navigation, systemsand methods for distributing a crowdsourced sparse map for autonomousvehicle navigation, systems and methods for navigating a vehicle using acrowdsourced sparse map, systems and methods for aligning crowdsourcedmap data, systems and methods for crowdsourcing road surface informationcollection, and systems and methods for vehicle localization using lanemeasurements.

Background Information

As technology continues to advance, the goal of a fully autonomousvehicle that is capable of navigating on roadways is on the horizon.Autonomous vehicles may need to take into account a variety of factorsand make appropriate decisions based on those factors to safely andaccurately reach an intended destination. For example, an autonomousvehicle may need to process and interpret visual information (e.g.,information captured from a camera) and may also use informationobtained from other sources (e.g., from a GPS device, a speed sensor, anaccelerometer, a suspension sensor, etc.). At the same time, in order tonavigate to a destination, an autonomous vehicle may also need toidentify its location within a particular roadway (e.g., a specific lanewithin a multi-lane road), navigate alongside other vehicles, avoidobstacles and pedestrians, observe traffic signals and signs, and travelfrom on road to another road at appropriate intersections orinterchanges. Harnessing and interpreting vast volumes of informationcollected by an autonomous vehicle as the vehicle travels to itsdestination poses a multitude of design challenges. The sheer quantityof data (e.g., captured image data, map data, GPS data, sensor data,etc.) that an autonomous vehicle may need to analyze, access, and/orstore poses challenges that can in fact limit or even adversely affectautonomous navigation. Furthermore, if an autonomous vehicle relies ontraditional mapping technology to navigate, the sheer volume of dataneeded to store and update the map poses daunting challenges.

In addition to the collection of data for updating the map, autonomousvehicles must be able to use the map for navigation. Accordingly, thesize and detail of the map must be optimized, as well as theconstruction and transmission thereof.

SUMMARY

Embodiments consistent with the present disclosure provide systems andmethods for autonomous vehicle navigation. The disclosed embodiments mayuse cameras to provide autonomous vehicle navigation features. Forexample, consistent with the disclosed embodiments, the disclosedsystems may include one, two, or more cameras that monitor theenvironment of a vehicle. The disclosed systems may provide anavigational response based on, for example, an analysis of imagescaptured by one or more of the cameras. The disclosed systems may alsoprovide for constructing and navigating with a crowdsourced sparse map.Other disclosed systems may use relevant analysis of images to performlocalization that may supplement navigation with a sparse map. Thenavigational response may also take into account other data including,for example, global positioning system (GPS) data, sensor data (e.g.,from an accelerometer, a speed sensor, a suspension sensor, etc.),and/or other map data.

As explained above, in some embodiments, the disclosed systems andmethods may use a sparse map for autonomous vehicle navigation. Forexample, the sparse map may provide sufficient information fornavigation without requiring excessive data storage.

In other embodiments, the disclosed systems and methods may construct aroad model for autonomous vehicle navigation. For example, the disclosedsystems and methods may allow for crowdsourcing a sparse map forautonomous vehicle navigation, distributing a crowdsourced sparse mapfor autonomous vehicle navigation and aligning crowdsourced map data.Some disclosed systems and methods may additionally or alternativelycrowdsource road surface information collection.

In yet other embodiments, the disclosed systems and methods may use asparse road model for autonomous vehicle navigation. For example, thedisclosed systems and methods may provide for navigating a vehicle usinga crowdsourced sparse map.

In still yet other embodiments, the disclosed systems and methods mayprovide adaptive autonomous navigation. For example, disclosed systemsand methods may provide for localization of a vehicle using lanemeasurements.

In some embodiments, a non-transitory computer-readable medium mayinclude a sparse map for autonomous vehicle navigation along a roadsegment. The sparse map may comprise at least one line representation ofa road surface feature extending along the road segment and a pluralityof landmarks associated with the road segment. Each line representationmay represent a path along the road segment substantially correspondingwith the road surface feature, and the road surface feature may beidentified through image analysis of a plurality of images acquired asone or more vehicles traverse the road segment.

In other embodiments, a system for generating a sparse map forautonomous vehicle navigation along a road segment may comprise at leastone processing device. The at least one processing device may beconfigured to receive a plurality of images acquired as one or morevehicles traverse the road segment; identify, based on the plurality ofimages, at least one line representation of a road surface featureextending along the road segment; and identify, based on the pluralityof images, a plurality of landmarks associated with the road segment.Each line representation may represent a path along the road segmentsubstantially corresponding with the road surface feature.

In still other embodiments, a method for generating a sparse mapautonomous vehicle navigation along a road segment may comprisereceiving a plurality of images acquired as one or more vehiclestraverse the road segment; identifying, based on the plurality ofimages, at least one line representation of a road surface featureextending along the road segment; and identifying, based on theplurality of images, a plurality of landmarks associated with the roadsegment. Each line representation may represent a path along the roadsegment substantially corresponding with the road surface feature.

In some embodiments, a method of generating a road navigation model foruse in autonomous vehicle navigation may comprise receiving, by aserver, navigation information from a plurality of vehicles. Thenavigation information from the plurality of vehicles may be associatedwith a common road segment. The method may further comprise storing, bythe server, the navigation information associated with the common roadsegment, and generating, by the server, at least a portion of anautonomous vehicle road navigation model for the common road segmentbased on the navigation information from the plurality of vehicles. Theautonomous vehicle road navigation model for the common road segment mayinclude at least one line representation of a road surface featureextending along the common road segment, and each line representationmay represent a path along the common road segment substantiallycorresponding with the road surface feature. Moreover, the road surfacefeature may be identified through image analysis of a plurality ofimages acquired as the plurality of vehicles traverse the common roadsegment. The method may further comprise distributing, by the server,the autonomous vehicle road navigation model to one or more autonomousvehicles for use in autonomously navigating the one or more autonomousvehicles along the common road segment.

In other embodiments, a system for generating a road navigation modelfor use in autonomous vehicle navigation may comprise at least onenetwork interface, at least one non-transitory storage medium, and atleast one processing device. The at least one processing device may beconfigured to receive, using the network interface, navigationinformation from a plurality of vehicles. The navigation informationfrom the plurality of vehicles may be associated with a common roadsegment. The at least one processing device may be further configured tostore, on the non-transitory storage medium, the navigation informationassociated with the common road segment, and generate at least a portionof an autonomous vehicle road navigation model for the common roadsegment based on the navigation information from the plurality ofvehicles. The autonomous vehicle road navigation model for the commonroad segment may include at least one line representation of a roadsurface feature extending along the common road segment, and each linerepresentation may represent a path along the common road segmentsubstantially corresponding with the road surface feature. Moreover, theroad surface feature may be identified through image analysis of aplurality of images acquired as the plurality of vehicles traverse thecommon road segment. The at least one processing device may be furtherconfigured to distribute, using the network interface, the autonomousvehicle road navigation model to one or more autonomous vehicles for usein autonomously navigating the one or more autonomous vehicles along thecommon road segment.

In some embodiments, a system for autonomously navigating a vehiclealong a road segment may comprise at least one processing device. The atleast one processing device may be configured to receive a sparse mapmodel. The sparse map model may include at least one line representationof a road surface feature extending along the road segment, and eachline representation may represent a path along the road segmentsubstantially corresponding with the road surface feature. The at leastone processing device may be further configured to receive from acamera, at least one image representative of an environment of thevehicle, analyze the sparse map model and the at least one imagereceived from the camera, and determine an autonomous navigationalresponse for the vehicle based on the analysis of the sparse map modeland the at least one image received from the camera.

In other embodiments, a method for autonomously navigating a vehiclealong a road segment may comprise receiving a sparse map model. Thesparse map model may include at least one line representation of a roadsurface feature extending along the road segment, and each linerepresentation may represent a path along the road segment substantiallycorresponding with the road surface feature. The method may furthercomprise receiving from a camera, at least one image representative ofan environment of the vehicle, analyzing the sparse map model and the atleast one image received from the camera, and determining an autonomousnavigational response for the vehicle based on the analysis of thesparse map model and the at least one image received from the camera.

In some embodiments, a method of determining a line representation of aroad surface feature extending along a road segment, where the linerepresentation of the road surface feature is configured for use inautonomous vehicle navigation, may comprise receiving, by a server, afirst set of drive data including position information associated withthe road surface feature, and receiving, by a server, a second set ofdrive data including position information associated with the roadsurface feature. The position information may be determined based onanalysis of images of the road segment. The method may further comprisesegmenting the first set of drive data into first drive patches andsegmenting the second set of drive data into second drive patches;longitudinally aligning the first set of drive data with the second setof drive data within corresponding patches; and determining the linerepresentation of the road surface feature based on the longitudinallyaligned first and second drive data in the first and second draftpatches.

In other embodiments, a system for determining a line representation ofa road surface feature extending along a road segment, where the linerepresentation of the road surface feature is configured for use inautonomous vehicle navigation, may comprise at least one processingdevice. The at least one processing device may be configured to receivea first set of drive data including position information associated withthe road surface feature, and receive a second set of drive dataincluding position information associated with the road surface feature.The position information may be determined based on analysis of imagesof the road segment. The at least one processing device may be furtherconfigured to segment the first set of drive data into first drivepatches and segment the second set of drive data into second drivepatches; longitudinally align the first set of drive data with thesecond set of drive data within corresponding patches; and determine theline representation of the road surface feature based on thelongitudinally aligned first and second drive data in the first andsecond draft patches.

In some embodiments, a system for collecting road surface informationfor a road segment may comprise at least one processing device. The atleast one processing device may be configured to receive, from a camera,at least one image representative of a portion of the road segment, andidentify in the at least one image at least one road surface featurealong the portion of the road segment. The at least one processingdevice may be further configured to determine a plurality of locationsassociated with the road surface feature according to a local coordinatesystem of the vehicle, and transmit the determined plurality oflocations from the vehicle to a server. The determined locations may beconfigured to enable determination by the server of a linerepresentation of the road surface feature extending along the roadsegment, and the line representation may represent a path along the roadsegment substantially corresponding with the road surface feature.

In other embodiments, a method of collecting road surface informationfor a road segment may comprise receiving, from a camera, at least oneimage representative of a portion of the road segment, and identifyingin the at least one image at least one road surface feature along theportion of the road segment. The method may further comprise determininga plurality of locations associated with the road surface featureaccording to a local coordinate system of the vehicle, and transmittingthe determined plurality of locations from the vehicle to a server. Thedetermined locations may be configured to enable determination by theserver of a line representation of the road surface feature extendingalong the road segment, and the line representation may represent a pathalong the road segment substantially corresponding with the road surfacefeature.

In some embodiments, a system for correcting a position of a vehiclenavigating a road segment may comprise at least one processing device.The at least one processing device may be configured to determine, basedon an output of at least one navigational sensor, a measured position ofthe vehicle along a predetermined road model trajectory. Thepredetermined road model trajectory may be associated with the roadsegment. The at least one processing device may be further configured toreceive, from an image capture device, at least one image representativeof an environment of the vehicle, and analyze the at least one image toidentify at least one lane marking. The at least one lane marking may beassociated with a lane of travel along the road segment. The at leastone processing device may be further configured to determine, based onthe at least one image, a distance from the vehicle to the at least onelane marking, determine an estimated offset of the vehicle from thepredetermined road model trajectory based on the measured position ofthe vehicle and the determined distance, and determine an autonomoussteering action for the vehicle based on the estimated offset to correctthe position of the vehicle.

In other embodiments, a method for correcting a position of a vehiclenavigating a road segment may comprise determining, based on an outputof at least one navigational sensor, a measured position of the vehiclealong a predetermined road model trajectory. The predetermined roadmodel trajectory may be associated with the road segment. The method mayfurther comprise receiving, from an image capture device, at least oneimage representative of an environment of the vehicle, and analyzing theat least one image to identify at least one lane marking. The at leastone lane marking may be associated with a lane of travel along the roadsegment. The method may further comprise determining, based on the atleast one image, a distance from the vehicle to the at least one lanemarking, determining an estimated offset of the vehicle from thepredetermined road model trajectory based on the measured position ofthe vehicle and the determined distance, and determining an autonomoussteering action for the vehicle based on the estimated offset to correctthe position of the vehicle.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processing device and perform any of themethods described herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1 is a diagrammatic representation of an exemplary systemconsistent with the disclosed embodiments.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle including a system consistent with the disclosed embodiments.

FIG. 2B is a diagrammatic top view representation of the vehicle andsystem shown in FIG. 2A consistent with the disclosed embodiments.

FIG. 2C is a diagrammatic top view representation of another embodimentof a vehicle including a system consistent with the disclosedembodiments.

FIG. 2D is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2E is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems consistent with the disclosed embodiments.

FIG. 3A is a diagrammatic representation of an interior of a vehicleincluding a rearview mirror and a user interface for a vehicle imagingsystem consistent with the disclosed embodiments.

FIG. 3B is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 3C is an illustration of the camera mount shown in FIG. 3B from adifferent perspective consistent with the disclosed embodiments.

FIG. 3D is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 4 is an exemplary block diagram of a memory configured to storeinstructions for performing one or more operations consistent with thedisclosed embodiments.

FIG. 5A is a flowchart showing an exemplary process for causing one ormore navigational responses based on monocular image analysis consistentwith disclosed embodiments.

FIG. 5B is a flowchart showing an exemplary process for detecting one ormore vehicles and/or pedestrians in a set of images consistent with thedisclosed embodiments.

FIG. 5C is a flowchart showing an exemplary process for detecting roadmarks and/or lane geometry information in a set of images consistentwith the disclosed embodiments.

FIG. 5D is a flowchart showing an exemplary process for detectingtraffic lights in a set of images consistent with the disclosedembodiments.

FIG. 5E is a flowchart showing an exemplary process for causing one ormore navigational responses based on a vehicle path consistent with thedisclosed embodiments.

FIG. 5F is a flowchart showing an exemplary process for determiningwhether a leading vehicle is changing lanes consistent with thedisclosed embodiments.

FIG. 6 is a flowchart showing an exemplary process for causing one ormore navigational responses based on stereo image analysis consistentwith the disclosed embodiments.

FIG. 7 is a flowchart showing an exemplary process for causing one ormore navigational responses based on an analysis of three sets of imagesconsistent with the disclosed embodiments.

FIG. 8 shows a sparse map for providing autonomous vehicle navigation,consistent with the disclosed embodiments.

FIG. 9A illustrates a polynomial representation of a portions of a roadsegment consistent with the disclosed embodiments.

FIG. 9B illustrates a curve in three-dimensional space representing atarget trajectory of a vehicle, for a particular road segment, includedin a sparse map consistent with the disclosed embodiments.

FIG. 10 illustrates example landmarks that may be included in sparse mapconsistent with the disclosed embodiments.

FIG. 11A shows polynomial representations of trajectories consistentwith the disclosed embodiments.

FIGS. 11B and 11C show target trajectories along a multi-lane roadconsistent with disclosed embodiments.

FIG. 11D shows an example road signature profile consistent withdisclosed embodiments.

FIG. 12 is a schematic illustration of a system that uses crowd sourcingdata received from a plurality of vehicles for autonomous vehiclenavigation, consistent with the disclosed embodiments.

FIG. 13 illustrates an example autonomous vehicle road navigation modelrepresented by a plurality of three dimensional splines, consistent withthe disclosed embodiments.

FIG. 14 shows a map skeleton generated from combining locationinformation from many drives, consistent with the disclosed embodiments.

FIG. 15 shows an example of a longitudinal alignment of two drives withexample signs as landmarks, consistent with the disclosed embodiments.

FIG. 16 shows an example of a longitudinal alignment of many drives withan example sign as a landmark, consistent with the disclosedembodiments.

FIG. 17 is a schematic illustration of a system for generating drivedata using a camera, a vehicle, and a server, consistent with thedisclosed embodiments.

FIG. 18 is a schematic illustration of a system for crowdsourcing asparse map, consistent with the disclosed embodiments.

FIG. 19 is a flowchart showing an exemplary process for generating asparse map for autonomous vehicle navigation along a road segment,consistent with the disclosed embodiments.

FIG. 20 illustrates a block diagram of a server consistent with thedisclosed embodiments.

FIG. 21 illustrates a block diagram of a memory consistent with thedisclosed embodiments.

FIG. 22 illustrates a process of clustering vehicle trajectoriesassociated with vehicles, consistent with the disclosed embodiments.

FIG. 23 illustrates a navigation system for a vehicle, which may be usedfor autonomous navigation, consistent with the disclosed embodiments.

FIG. 24 is a flowchart showing an example process for generating a roadnavigation model for use in autonomous vehicle navigation, consistentwith the disclosed embodiments.

FIG. 25 illustrates a block diagram of a memory consistent with thedisclosed embodiments.

FIG. 26 is a flowchart showing an example process for autonomouslynavigating a vehicle along a road segment, consistent with the disclosedembodiments.

FIG. 27 illustrates a block diagram of a memory consistent with thedisclosed embodiments.

FIG. 28A illustrates an example of drive data from four separate drives,consistent with the disclosed embodiments.

FIG. 28B illustrates an example of drive data from five separate drives,consistent with the disclosed embodiments.

FIG. 28BC illustrates an example of vehicle paths determined from drivedata from five separate drives, consistent with the disclosedembodiments.

FIG. 29 is a flowchart showing an example process for determining a linerepresentation of a road surface feature extending along a road segment,consistent with the disclosed embodiments.

FIG. 30 illustrates a block diagram of a memory consistent with thedisclosed embodiments.

FIG. 31 is a flowchart showing an example process for collecting roadsurface information for a road segment, consistent with the disclosedembodiments.

FIG. 32 illustrates a block diagram of a memory consistent with thedisclosed embodiments.

FIG. 33A illustrates an example of a vehicle traversing a lane withoutusing lane markings.

FIG. 33B illustrates the example of FIG. 33A after position and headingof the vehicle have drifted.

FIG. 33C illustrates the example of FIG. 33B after position and headinghave further drifted and the expected location of a landmarksignificantly differs from its actual location.

FIG. 34A illustrates an example of a vehicle traversing a lane withoutusing lane markings, consistent with the disclosed embodiments.

FIG. 34B illustrates the example of FIG. 34A with decreased drift ofposition and heading, consistent with the disclosed embodiments.

FIG. 34C illustrates the example of FIG. 34B with the expected locationof a landmark significantly aligning with its actual location,consistent with the disclosed embodiments.

FIG. 35 is a flowchart showing an example process for correcting aposition of a vehicle navigating a road segment, consistent with thedisclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

Autonomous Vehicle Overview

As used throughout this disclosure, the term “autonomous vehicle” refersto a vehicle capable of implementing at least one navigational changewithout driver input. A “navigational change” refers to a change in oneor more of steering, braking, or acceleration of the vehicle. To beautonomous, a vehicle need not be fully automatic (e.g., fully operationwithout a driver or without driver input). Rather, an autonomous vehicleincludes those that can operate under driver control during certain timeperiods and without driver control during other time periods. Autonomousvehicles may also include vehicles that control only some aspects ofvehicle navigation, such as steering (e.g., to maintain a vehicle coursebetween vehicle lane constraints), but may leave other aspects to thedriver (e.g., braking). In some cases, autonomous vehicles may handlesome or all aspects of braking, speed control, and/or steering of thevehicle.

As human drivers typically rely on visual cues and observations order tocontrol a vehicle, transportation infrastructures are built accordingly,with lane markings, traffic signs, and traffic lights are all designedto provide visual information to drivers. In view of these designcharacteristics of transportation infrastructures, an autonomous vehiclemay include a camera and a processing unit that analyzes visualinformation captured from the environment of the vehicle. The visualinformation may include, for example, components of the transportationinfrastructure (e.g., lane markings, traffic signs, traffic lights,etc.) that are observable by drivers and other obstacles (e.g., othervehicles, pedestrians, debris, etc.). Additionally, an autonomousvehicle may also use stored information, such as information thatprovides a model of the vehicle's environment when navigating. Forexample, the vehicle may use GPS data, sensor data (e.g., from anaccelerometer, a speed sensor, a suspension sensor, etc.), and/or othermap data to provide information related to its environment while thevehicle is traveling, and the vehicle (as well as other vehicles) mayuse the information to localize itself on the model.

In some embodiments in this disclosure, an autonomous vehicle may useinformation obtained while navigating (e.g., from a camera, GPS device,an accelerometer, a speed sensor, a suspension sensor, etc.). In otherembodiments, an autonomous vehicle may use information obtained frompast navigations by the vehicle (or by other vehicles) while navigating.In yet other embodiments, an autonomous vehicle may use a combination ofinformation obtained while navigating and information obtained from pastnavigations. The following sections provide an overview of a systemconsistent with the disclosed embodiments, following by an overview of aforward-facing imaging system and methods consistent with the system.The sections that follow disclose systems and methods for constructing,using, and updating a sparse map for autonomous vehicle navigation.

System Overview

FIG. 1 is a block diagram representation of a system 100 consistent withthe exemplary disclosed embodiments. System 100 may include variouscomponents depending on the requirements of a particular implementation.In some embodiments, system 100 may include a processing unit 110, animage acquisition unit 120, a position sensor 130, one or more memoryunits 140, 150, a map database 160, a user interface 170, and a wirelesstransceiver 172. Processing unit 110 may include one or more processingdevices. In some embodiments, processing unit 110 may include anapplications processor 180, an image processor 190, or any othersuitable processing device. Similarly, image acquisition unit 120 mayinclude any number of image acquisition devices and components dependingon the requirements of a particular application. In some embodiments,image acquisition unit 120 may include one or more image capture devices(e.g., cameras), such as image capture device 122, image capture device124, and image capture device 126. System 100 may also include a datainterface 128 communicatively connecting processing device 110 to imageacquisition device 120. For example, data interface 128 may include anywired and/or wireless link or links for transmitting image data acquiredby image accusation device 120 to processing unit 110.

Wireless transceiver 172 may include one or more devices configured toexchange transmissions over an air interface to one or more networks(e.g., cellular, the Internet, etc.) by use of a radio frequency,infrared frequency, magnetic field, or an electric field. Wirelesstransceiver 172 may use any known standard to transmit and/or receivedata (e.g., Wi-Fi, Bluetooth®, Bluetooth Smart, 802.15.4, ZigBee, etc.).Such transmissions can include communications from the host vehicle toone or more remotely located servers. Such transmissions may alsoinclude communications (one-way or two-way) between the host vehicle andone or more target vehicles in an environment of the host vehicle (e.g.,to facilitate coordination of navigation of the host vehicle in view ofor together with target vehicles in the environment of the hostvehicle), or even a broadcast transmission to unspecified recipients ina vicinity of the transmitting vehicle.

Both applications processor 180 and image processor 190 may includevarious types of processing devices. For example, either or both ofapplications processor 180 and image processor 190 may include amicroprocessor, preprocessors (such as an image preprocessor), agraphics processing unit (GPU), a central processing unit (CPU), supportcircuits, digital signal processors, integrated circuits, memory, or anyother types of devices suitable for running applications and for imageprocessing and analysis. In some embodiments, applications processor 180and/or image processor 190 may include any type of single or multi-coreprocessor, mobile device microcontroller, central processing unit, etc.Various processing devices may be used, including, for example,processors available from manufacturers such as Intel®, AMD®, etc., orGPUs available from manufacturers such as NVIDIA®, ATI®, etc. and mayinclude various architectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, applications processor 180 and/or image processor190 may include any of the EyeQ series of processor chips available fromMobileye®. These processor designs each include multiple processingunits with local memory and instruction sets. Such processors mayinclude video inputs for receiving image data from multiple imagesensors and may also include video out capabilities. In one example, theEyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2®architecture consists of two floating point, hyper-thread 32-bit RISCCPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), threeVector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller,128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bitVideo output controllers, 16 channels DMA and several peripherals. TheMIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the secondMIPS34K CPU and the multi-channel DMA as well as the other peripherals.The five VCEs, three VMP® and the MIPS34K CPU can perform intensivevision computations required by multi-function bundle applications. Inanother example, the EyeQ3®, which is a third generation processor andis six times more powerful that the EyeQ2®, may be used in the disclosedembodiments. In other examples, the EyeQ4® and/or the EyeQ5® may be usedin the disclosed embodiments. Of course, any newer or future EyeQprocessing devices may also be used together with the disclosedembodiments.

Any of the processing devices disclosed herein may be configured toperform certain functions. Configuring a processing device, such as anyof the described EyeQ processors or other controller or microprocessor,to perform certain functions may include programming of computerexecutable instructions and making those instructions available to theprocessing device for execution during operation of the processingdevice. In some embodiments, configuring a processing device may includeprogramming the processing device directly with architecturalinstructions. For example, processing devices such as field-programmablegate arrays (FPGAs), application-specific integrated circuits (ASICs),and the like may be configured using, for example, one or more hardwaredescription languages (HDLs).

In other embodiments, configuring a processing device may includestoring executable instructions on a memory that is accessible to theprocessing device during operation. For example, the processing devicemay access the memory to obtain and execute the stored instructionsduring operation. In either case, the processing device configured toperform the sensing, image analysis, and/or navigational functionsdisclosed herein represents a specialized hardware-based system incontrol of multiple hardware based components of a host vehicle.

While FIG. 1 depicts two separate processing devices included inprocessing unit 110, more or fewer processing devices may be used. Forexample, in some embodiments, a single processing device may be used toaccomplish the tasks of applications processor 180 and image processor190. In other embodiments, these tasks may be performed by more than twoprocessing devices. Further, in some embodiments, system 100 may includeone or more of processing unit 110 without including other components,such as image acquisition unit 120.

Processing unit 110 may comprise various types of devices. For example,processing unit 110 may include various devices, such as a controller,an image preprocessor, a central processing unit (CPU), a graphicsprocessing unit (GPU), support circuits, digital signal processors,integrated circuits, memory, or any other types of devices for imageprocessing and analysis. The image preprocessor may include a videoprocessor for capturing, digitizing and processing the imagery from theimage sensors. The CPU may comprise any number of microcontrollers ormicroprocessors. The GPU may also comprise any number ofmicrocontrollers or microprocessors. The support circuits may be anynumber of circuits generally well known in the art, including cache,power supply, clock and input-output circuits. The memory may storesoftware that, when executed by the processor, controls the operation ofthe system. The memory may include databases and image processingsoftware. The memory may comprise any number of random access memories,read only memories, flash memories, disk drives, optical storage, tapestorage, removable storage and other types of storage. In one instance,the memory may be separate from the processing unit 110. In anotherinstance, the memory may be integrated into the processing unit 110.

Each memory 140, 150 may include software instructions that whenexecuted by a processor (e.g., applications processor 180 and/or imageprocessor 190), may control operation of various aspects of system 100.These memory units may include various databases and image processingsoftware, as well as a trained system, such as a neural network, or adeep neural network, for example. The memory units may include randomaccess memory (RAM), read only memory (ROM), flash memory, disk drives,optical storage, tape storage, removable storage and/or any other typesof storage. In some embodiments, memory units 140, 150 may be separatefrom the applications processor 180 and/or image processor 190. In otherembodiments, these memory units may be integrated into applicationsprocessor 180 and/or image processor 190.

Position sensor 130 may include any type of device suitable fordetermining a location associated with at least one component of system100. In some embodiments, position sensor 130 may include a GPSreceiver. Such receivers can determine a user position and velocity byprocessing signals broadcasted by global positioning system satellites.Position information from position sensor 130 may be made available toapplications processor 180 and/or image processor 190.

In some embodiments, system 100 may include components such as a speedsensor (e.g., a tachometer, a speedometer) for measuring a speed ofvehicle 200 and/or an accelerometer (either single axis or multiaxis)for measuring acceleration of vehicle 200.

User interface 170 may include any device suitable for providinginformation to or for receiving inputs from one or more users of system100. In some embodiments, user interface 170 may include user inputdevices, including, for example, a touchscreen, microphone, keyboard,pointer devices, track wheels, cameras, knobs, buttons, etc. With suchinput devices, a user may be able to provide information inputs orcommands to system 100 by typing instructions or information, providingvoice commands, selecting menu options on a screen using buttons,pointers, or eye-tracking capabilities, or through any other suitabletechniques for communicating information to system 100.

User interface 170 may be equipped with one or more processing devicesconfigured to provide and receive information to or from a user andprocess that information for use by, for example, applications processor180. In some embodiments, such processing devices may executeinstructions for recognizing and tracking eye movements, receiving andinterpreting voice commands, recognizing and interpreting touches and/orgestures made on a touchscreen, responding to keyboard entries or menuselections, etc. In some embodiments, user interface 170 may include adisplay, speaker, tactile device, and/or any other devices for providingoutput information to a user.

Map database 160 may include any type of database for storing map datauseful to system 100. In some embodiments, map database 160 may includedata relating to the position, in a reference coordinate system, ofvarious items, including roads, water features, geographic features,businesses, points of interest, restaurants, gas stations, etc. Mapdatabase 160 may store not only the locations of such items, but alsodescriptors relating to those items, including, for example, namesassociated with any of the stored features. In some embodiments, mapdatabase 160 may be physically located with other components of system100. Alternatively or additionally, map database 160 or a portionthereof may be located remotely with respect to other components ofsystem 100 (e.g., processing unit 110). In such embodiments, informationfrom map database 160 may be downloaded over a wired or wireless dataconnection to a network (e.g., over a cellular network and/or theInternet, etc.). In some cases, map database 160 may store a sparse datamodel including polynomial representations of certain road features(e.g., lane markings) or target trajectories for the host vehicle.Systems and methods of generating such a map are discussed below withreferences to FIGS. 8-19.

Image capture devices 122, 124, and 126 may each include any type ofdevice suitable for capturing at least one image from an environment.Moreover, any number of image capture devices may be used to acquireimages for input to the image processor. Some embodiments may includeonly a single image capture device, while other embodiments may includetwo, three, or even four or more image capture devices. Image capturedevices 122, 124, and 126 will be further described with reference toFIGS. 2B-2E, below.

System 100, or various components thereof, may be incorporated intovarious different platforms. In some embodiments, system 100 may beincluded on a vehicle 200, as shown in FIG. 2A. For example, vehicle 200may be equipped with a processing unit 110 and any of the othercomponents of system 100, as described above relative to FIG. 1. Whilein some embodiments vehicle 200 may be equipped with only a single imagecapture device (e.g., camera), in other embodiments, such as thosediscussed in connection with FIGS. 2B-2E, multiple image capture devicesmay be used. For example, either of image capture devices 122 and 124 ofvehicle 200, as shown in FIG. 2A, may be part of an ADAS (AdvancedDriver Assistance Systems) imaging set.

The image capture devices included on vehicle 200 as part of the imageacquisition unit 120 may be positioned at any suitable location. In someembodiments, as shown in FIGS. 2A-2E and 3A-3C, image capture device 122may be located in the vicinity of the rearview mirror. This position mayprovide a line of sight similar to that of the driver of vehicle 200,which may aid in determining what is and is not visible to the driver.Image capture device 122 may be positioned at any location near therearview mirror, but placing image capture device 122 on the driver sideof the mirror may further aid in obtaining images representative of thedriver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit120 may also be used. For example, image capture device 124 may belocated on or in a bumper of vehicle 200. Such a location may beespecially suitable for image capture devices having a wide field ofview. The line of sight of bumper-located image capture devices can bedifferent from that of the driver and, therefore, the bumper imagecapture device and driver may not always see the same objects. The imagecapture devices (e.g., image capture devices 122, 124, and 126) may alsobe located in other locations. For example, the image capture devicesmay be located on or in one or both of the side mirrors of vehicle 200,on the roof of vehicle 200, on the hood of vehicle 200, on the trunk ofvehicle 200, on the sides of vehicle 200, mounted on, positioned behind,or positioned in front of any of the windows of vehicle 200, and mountedin or near light figures on the front and/or back of vehicle 200, etc.

In addition to image capture devices, vehicle 200 may include variousother components of system 100. For example, processing unit 110 may beincluded on vehicle 200 either integrated with or separate from anengine control unit (ECU) of the vehicle. Vehicle 200 may also beequipped with a position sensor 130, such as a GPS receiver and may alsoinclude a map database 160 and memory units 140 and 150.

As discussed earlier, wireless transceiver 172 may and/or receive dataover one or more networks (e.g., cellular networks, the Internet, etc.).For example, wireless transceiver 172 may upload data collected bysystem 100 to one or more servers, and download data from the one ormore servers. Via wireless transceiver 172, system 100 may receive, forexample, periodic or on demand updates to data stored in map database160, memory 140, and/or memory 150. Similarly, wireless transceiver 172may upload any data (e.g., images captured by image acquisition unit120, data received by position sensor 130 or other sensors, vehiclecontrol systems, etc.) from by system 100 and/or any data processed byprocessing unit 110 to the one or more servers.

System 100 may upload data to a server (e.g., to the cloud) based on aprivacy level setting. For example, system 100 may implement privacylevel settings to regulate or limit the types of data (includingmetadata) sent to the server that may uniquely identify a vehicle and ordriver/owner of a vehicle. Such settings may be set by user via, forexample, wireless transceiver 172, be initialized by factory defaultsettings, or by data received by wireless transceiver 172.

In some embodiments, system 100 may upload data according to a “high”privacy level, and under setting a setting, system 100 may transmit data(e.g., location information related to a route, captured images, etc.)without any details about the specific vehicle and/or driver/owner. Forexample, when uploading data according to a “high” privacy setting,system 100 may not include a vehicle identification number (VIN) or aname of a driver or owner of the vehicle, and may instead of transmitdata, such as captured images and/or limited location informationrelated to a route.

Other privacy levels are contemplated. For example, system 100 maytransmit data to a server according to an “intermediate” privacy leveland include additional information not included under a “high” privacylevel, such as a make and/or model of a vehicle and/or a vehicle type(e.g., a passenger vehicle, sport utility vehicle, truck, etc.). In someembodiments, system 100 may upload data according to a “low” privacylevel. Under a “low” privacy level setting, system 100 may upload dataand include information sufficient to uniquely identify a specificvehicle, owner/driver, and/or a portion or entirely of a route traveledby the vehicle. Such “low” privacy level data may include one or moreof, for example, a VIN, a driver/owner name, an origination point of avehicle prior to departure, an intended destination of the vehicle, amake and/or model of the vehicle, a type of the vehicle, etc.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle imaging system consistent with the disclosed embodiments. FIG.2B is a diagrammatic top view illustration of the embodiment shown inFIG. 2A. As illustrated in FIG. 2B, the disclosed embodiments mayinclude a vehicle 200 including in its body a system 100 with a firstimage capture device 122 positioned in the vicinity of the rearviewmirror and/or near the driver of vehicle 200, a second image capturedevice 124 positioned on or in a bumper region (e.g., one of bumperregions 210) of vehicle 200, and a processing unit 110.

As illustrated in FIG. 2C, image capture devices 122 and 124 may both bepositioned in the vicinity of the rearview mirror and/or near the driverof vehicle 200. Additionally, while two image capture devices 122 and124 are shown in FIGS. 2B and 2C, it should be understood that otherembodiments may include more than two image capture devices. Forexample, in the embodiments shown in FIGS. 2D and 2E, first, second, andthird image capture devices 122, 124, and 126, are included in thesystem 100 of vehicle 200.

As illustrated in FIG. 2D, image capture device 122 may be positioned inthe vicinity of the rearview mirror and/or near the driver of vehicle200, and image capture devices 124 and 126 may be positioned on or in abumper region (e.g., one of bumper regions 210) of vehicle 200. And asshown in FIG. 2E, image capture devices 122, 124, and 126 may bepositioned in the vicinity of the rearview mirror and/or near the driverseat of vehicle 200. The disclosed embodiments are not limited to anyparticular number and configuration of the image capture devices, andthe image capture devices may be positioned in any appropriate locationwithin and/or on vehicle 200.

It is to be understood that the disclosed embodiments are not limited tovehicles and could be applied in other contexts. It is also to beunderstood that disclosed embodiments are not limited to a particulartype of vehicle 200 and may be applicable to all types of vehiclesincluding automobiles, trucks, trailers, and other types of vehicles.

The first image capture device 122 may include any suitable type ofimage capture device. Image capture device 122 may include an opticalaxis. In one instance, the image capture device 122 may include anAptina M9V024 WVGA sensor with a global shutter. In other embodiments,image capture device 122 may provide a resolution of 1280×960 pixels andmay include a rolling shutter. Image capture device 122 may includevarious optical elements. In some embodiments one or more lenses may beincluded, for example, to provide a desired focal length and field ofview for the image capture device. In some embodiments, image capturedevice 122 may be associated with a 6 mm lens or a 12 mm lens. In someembodiments, image capture device 122 may be configured to captureimages having a desired field-of-view (FOV) 202, as illustrated in FIG.2D. For example, image capture device 122 may be configured to have aregular FOV, such as within a range of 40 degrees to 56 degrees,including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater.Alternatively, image capture device 122 may be configured to have anarrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or36 degree FOV. In addition, image capture device 122 may be configuredto have a wide FOV in the range of 100 to 180 degrees. In someembodiments, image capture device 122 may include a wide angle bumpercamera or one with up to a 180 degree FOV. In some embodiments, imagecapture device 122 may be a 7.2 M pixel image capture device with anaspect ratio of about 2:1 (e.g., H×V=3800×1900 pixels) with about 100degree horizontal FOV. Such an image capture device may be used in placeof a three image capture device configuration. Due to significant lensdistortion, the vertical FOV of such an image capture device may besignificantly less than 50 degrees in implementations in which the imagecapture device uses a radially symmetric lens. For example, such a lensmay not be radially symmetric which would allow for a vertical FOVgreater than 50 degrees with 100 degree horizontal FOV.

The first image capture device 122 may acquire a plurality of firstimages relative to a scene associated with the vehicle 200. Each of theplurality of first images may be acquired as a series of image scanlines, which may be captured using a rolling shutter. Each scan line mayinclude a plurality of pixels.

The first image capture device 122 may have a scan rate associated withacquisition of each of the first series of image scan lines. The scanrate may refer to a rate at which an image sensor can acquire image dataassociated with each pixel included in a particular scan line.

Image capture devices 122, 124, and 126 may contain any suitable typeand number of image sensors, including CCD sensors or CMOS sensors, forexample. In one embodiment, a CMOS image sensor may be employed alongwith a rolling shutter, such that each pixel in a row is read one at atime, and scanning of the rows proceeds on a row-by-row basis until anentire image frame has been captured. In some embodiments, the rows maybe captured sequentially from top to bottom relative to the frame.

In some embodiments, one or more of the image capture devices (e.g.,image capture devices 122, 124, and 126) disclosed herein may constitutea high resolution imager and may have a resolution greater than 5 Mpixel, 7 M pixel, 10 M pixel, or greater.

The use of a rolling shutter may result in pixels in different rowsbeing exposed and captured at different times, which may cause skew andother image artifacts in the captured image frame. On the other hand,when the image capture device 122 is configured to operate with a globalor synchronous shutter, all of the pixels may be exposed for the sameamount of time and during a common exposure period. As a result, theimage data in a frame collected from a system employing a global shutterrepresents a snapshot of the entire FOV (such as FOV 202) at aparticular time. In contrast, in a rolling shutter application, each rowin a frame is exposed and data is capture at different times. Thus,moving objects may appear distorted in an image capture device having arolling shutter. This phenomenon will be described in greater detailbelow.

The second image capture device 124 and the third image capturing device126 may be any type of image capture device. Like the first imagecapture device 122, each of image capture devices 124 and 126 mayinclude an optical axis. In one embodiment, each of image capturedevices 124 and 126 may include an Aptina M9V024 WVGA sensor with aglobal shutter. Alternatively, each of image capture devices 124 and 126may include a rolling shutter. Like image capture device 122, imagecapture devices 124 and 126 may be configured to include various lensesand optical elements. In some embodiments, lenses associated with imagecapture devices 124 and 126 may provide FOVs (such as FOVs 204 and 206)that are the same as, or narrower than, a FOV (such as FOV 202)associated with image capture device 122. For example, image capturedevices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26 degrees,23 degrees, 20 degrees, or less.

Image capture devices 124 and 126 may acquire a plurality of second andthird images relative to a scene associated with the vehicle 200. Eachof the plurality of second and third images may be acquired as a secondand third series of image scan lines, which may be captured using arolling shutter. Each scan line or row may have a plurality of pixels.Image capture devices 124 and 126 may have second and third scan ratesassociated with acquisition of each of image scan lines included in thesecond and third series.

Each image capture device 122, 124, and 126 may be positioned at anysuitable position and orientation relative to vehicle 200. The relativepositioning of the image capture devices 122, 124, and 126 may beselected to aid in fusing together the information acquired from theimage capture devices. For example, in some embodiments, a FOV (such asFOV 204) associated with image capture device 124 may overlap partiallyor fully with a FOV (such as FOV 202) associated with image capturedevice 122 and a FOV (such as FOV 206) associated with image capturedevice 126.

Image capture devices 122, 124, and 126 may be located on vehicle 200 atany suitable relative heights. In one instance, there may be a heightdifference between the image capture devices 122, 124, and 126, whichmay provide sufficient parallax information to enable stereo analysis.For example, as shown in FIG. 2A, the two image capture devices 122 and124 are at different heights. There may also be a lateral displacementdifference between image capture devices 122, 124, and 126, givingadditional parallax information for stereo analysis by processing unit110, for example. The difference in the lateral displacement may bedenoted by d_(x), as shown in FIGS. 2C and 2D. In some embodiments, foreor aft displacement (e.g., range displacement) may exist between imagecapture devices 122, 124, and 126. For example, image capture device 122may be located 0.5 to 2 meters or more behind image capture device 124and/or image capture device 126. This type of displacement may enableone of the image capture devices to cover potential blind spots of theother image capture device(s).

Image capture devices 122 may have any suitable resolution capability(e.g., number of pixels associated with the image sensor), and theresolution of the image sensor(s) associated with the image capturedevice 122 may be higher, lower, or the same as the resolution of theimage sensor(s) associated with image capture devices 124 and 126. Insome embodiments, the image sensor(s) associated with image capturedevice 122 and/or image capture devices 124 and 126 may have aresolution of 640×480, 1024×768, 1280×960, or any other suitableresolution.

The frame rate (e.g., the rate at which an image capture device acquiresa set of pixel data of one image frame before moving on to capture pixeldata associated with the next image frame) may be controllable. Theframe rate associated with image capture device 122 may be higher,lower, or the same as the frame rate associated with image capturedevices 124 and 126. The frame rate associated with image capturedevices 122, 124, and 126 may depend on a variety of factors that mayaffect the timing of the frame rate. For example, one or more of imagecapture devices 122, 124, and 126 may include a selectable pixel delayperiod imposed before or after acquisition of image data associated withone or more pixels of an image sensor in image capture device 122, 124,and/or 126. Generally, image data corresponding to each pixel may beacquired according to a clock rate for the device (e.g., one pixel perclock cycle). Additionally, in embodiments including a rolling shutter,one or more of image capture devices 122, 124, and 126 may include aselectable horizontal blanking period imposed before or afteracquisition of image data associated with a row of pixels of an imagesensor in image capture device 122, 124, and/or 126. Further, one ormore of image capture devices 122, 124, and/or 126 may include aselectable vertical blanking period imposed before or after acquisitionof image data associated with an image frame of image capture device122, 124, and 126.

These timing controls may enable synchronization of frame ratesassociated with image capture devices 122, 124, and 126, even where theline scan rates of each are different. Additionally, as will bediscussed in greater detail below, these selectable timing controls,among other factors (e.g., image sensor resolution, maximum line scanrates, etc.) may enable synchronization of image capture from an areawhere the FOV of image capture device 122 overlaps with one or more FOVsof image capture devices 124 and 126, even where the field of view ofimage capture device 122 is different from the FOVs of image capturedevices 124 and 126.

Frame rate timing in image capture device 122, 124, and 126 may dependon the resolution of the associated image sensors. For example, assumingsimilar line scan rates for both devices, if one device includes animage sensor having a resolution of 640×480 and another device includesan image sensor with a resolution of 1280×960, then more time will berequired to acquire a frame of image data from the sensor having thehigher resolution.

Another factor that may affect the timing of image data acquisition inimage capture devices 122, 124, and 126 is the maximum line scan rate.For example, acquisition of a row of image data from an image sensorincluded in image capture device 122, 124, and 126 will require someminimum amount of time. Assuming no pixel delay periods are added, thisminimum amount of time for acquisition of a row of image data will berelated to the maximum line scan rate for a particular device. Devicesthat offer higher maximum line scan rates have the potential to providehigher frame rates than devices with lower maximum line scan rates. Insome embodiments, one or more of image capture devices 124 and 126 mayhave a maximum line scan rate that is higher than a maximum line scanrate associated with image capture device 122. In some embodiments, themaximum line scan rate of image capture device 124 and/or 126 may be1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate ofimage capture device 122.

In another embodiment, image capture devices 122, 124, and 126 may havethe same maximum line scan rate, but image capture device 122 may beoperated at a scan rate less than or equal to its maximum scan rate. Thesystem may be configured such that one or more of image capture devices124 and 126 operate at a line scan rate that is equal to the line scanrate of image capture device 122. In other instances, the system may beconfigured such that the line scan rate of image capture device 124and/or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times ormore than the line scan rate of image capture device 122.

In some embodiments, image capture devices 122, 124, and 126 may beasymmetric. That is, they may include cameras having different fields ofview (FOV) and focal lengths. The fields of view of image capturedevices 122, 124, and 126 may include any desired area relative to anenvironment of vehicle 200, for example. In some embodiments, one ormore of image capture devices 122, 124, and 126 may be configured toacquire image data from an environment in front of vehicle 200, behindvehicle 200, to the sides of vehicle 200, or combinations thereof.

Further, the focal length associated with each image capture device 122,124, and/or 126 may be selectable (e.g., by inclusion of appropriatelenses etc.) such that each device acquires images of objects at adesired distance range relative to vehicle 200. For example, in someembodiments image capture devices 122, 124, and 126 may acquire imagesof close-up objects within a few meters from the vehicle. Image capturedevices 122, 124, and 126 may also be configured to acquire images ofobjects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100m, 150 m, or more). Further, the focal lengths of image capture devices122, 124, and 126 may be selected such that one image capture device(e.g., image capture device 122) can acquire images of objectsrelatively close to the vehicle (e.g., within 10 m or within 20 m) whilethe other image capture devices (e.g., image capture devices 124 and126) can acquire images of more distant objects (e.g., greater than 20m, 50 m, 100 m, 150 m, etc.) from vehicle 200.

According to some embodiments, the FOV of one or more image capturedevices 122, 124, and 126 may have a wide angle. For example, it may beadvantageous to have a FOV of 140 degrees, especially for image capturedevices 122, 124, and 126 that may be used to capture images of the areain the vicinity of vehicle 200. For example, image capture device 122may be used to capture images of the area to the right or left ofvehicle 200 and, in such embodiments, it may be desirable for imagecapture device 122 to have a wide FOV (e.g., at least 140 degrees).

The field of view associated with each of image capture devices 122,124, and 126 may depend on the respective focal lengths. For example, asthe focal length increases, the corresponding field of view decreases.

Image capture devices 122, 124, and 126 may be configured to have anysuitable fields of view. In one particular example, image capture device122 may have a horizontal FOV of 46 degrees, image capture device 124may have a horizontal FOV of 23 degrees, and image capture device 126may have a horizontal FOV in between 23 and 46 degrees. In anotherinstance, image capture device 122 may have a horizontal FOV of 52degrees, image capture device 124 may have a horizontal FOV of 26degrees, and image capture device 126 may have a horizontal FOV inbetween 26 and 52 degrees. In some embodiments, a ratio of the FOV ofimage capture device 122 to the FOVs of image capture device 124 and/orimage capture device 126 may vary from 1.5 to 2.0. In other embodiments,this ratio may vary between 1.25 and 2.25.

System 100 may be configured so that a field of view of image capturedevice 122 overlaps, at least partially or fully, with a field of viewof image capture device 124 and/or image capture device 126. In someembodiments, system 100 may be configured such that the fields of viewof image capture devices 124 and 126, for example, fall within (e.g.,are narrower than) and share a common center with the field of view ofimage capture device 122. In other embodiments, the image capturedevices 122, 124, and 126 may capture adjacent FOVs or may have partialoverlap in their FOVs. In some embodiments, the fields of view of imagecapture devices 122, 124, and 126 may be aligned such that a center ofthe narrower FOV image capture devices 124 and/or 126 may be located ina lower half of the field of view of the wider FOV device 122.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems, consistent with the disclosed embodiments. As indicated in FIG.2F, vehicle 200 may include throttling system 220, braking system 230,and steering system 240. System 100 may provide inputs (e.g., controlsignals) to one or more of throttling system 220, braking system 230,and steering system 240 over one or more data links (e.g., any wiredand/or wireless link or links for transmitting data). For example, basedon analysis of images acquired by image capture devices 122, 124, and/or126, system 100 may provide control signals to one or more of throttlingsystem 220, braking system 230, and steering system 240 to navigatevehicle 200 (e.g., by causing an acceleration, a turn, a lane shift,etc.). Further, system 100 may receive inputs from one or more ofthrottling system 220, braking system 230, and steering system 24indicating operating conditions of vehicle 200 (e.g., speed, whethervehicle 200 is braking and/or turning, etc.). Further details areprovided in connection with FIGS. 4-7, below.

As shown in FIG. 3A, vehicle 200 may also include a user interface 170for interacting with a driver or a passenger of vehicle 200. Forexample, user interface 170 in a vehicle application may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver orpassenger of vehicle 200 may also use handles (e.g., located on or nearthe steering column of vehicle 200 including, for example, turn signalhandles), buttons (e.g., located on the steering wheel of vehicle 200),and the like, to interact with system 100. In some embodiments,microphone 350 may be positioned adjacent to a rearview mirror 310.Similarly, in some embodiments, image capture device 122 may be locatednear rearview mirror 310. In some embodiments, user interface 170 mayalso include one or more speakers 360 (e.g., speakers of a vehicle audiosystem). For example, system 100 may provide various notifications(e.g., alerts) via speakers 360.

FIGS. 3B-3D are illustrations of an exemplary camera mount 370configured to be positioned behind a rearview mirror (e.g., rearviewmirror 310) and against a vehicle windshield, consistent with disclosedembodiments. As shown in FIG. 3B, camera mount 370 may include imagecapture devices 122, 124, and 126. Image capture devices 124 and 126 maybe positioned behind a glare shield 380, which may be flush against thevehicle windshield and include a composition of film and/oranti-reflective materials. For example, glare shield 380 may bepositioned such that the shield aligns against a vehicle windshieldhaving a matching slope. In some embodiments, each of image capturedevices 122, 124, and 126 may be positioned behind glare shield 380, asdepicted, for example, in FIG. 3D. The disclosed embodiments are notlimited to any particular configuration of image capture devices 122,124, and 126, camera mount 370, and glare shield 380. FIG. 3C is anillustration of camera mount 370 shown in FIG. 3B from a frontperspective.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the foregoing disclosed embodiments. For example, not all componentsare essential for the operation of system 100. Further, any componentmay be located in any appropriate part of system 100 and the componentsmay be rearranged into a variety of configurations while providing thefunctionality of the disclosed embodiments. Therefore, the foregoingconfigurations are examples and, regardless of the configurationsdiscussed above, system 100 can provide a wide range of functionality toanalyze the surroundings of vehicle 200 and navigate vehicle 200 inresponse to the analysis.

As discussed below in further detail and consistent with variousdisclosed embodiments, system 100 may provide a variety of featuresrelated to autonomous driving and/or driver assist technology. Forexample, system 100 may analyze image data, position data (e.g., GPSlocation information), map data, speed data, and/or data from sensorsincluded in vehicle 200. System 100 may collect the data for analysisfrom, for example, image acquisition unit 120, position sensor 130, andother sensors. Further, system 100 may analyze the collected data todetermine whether or not vehicle 200 should take a certain action, andthen automatically take the determined action without humanintervention. For example, when vehicle 200 navigates without humanintervention, system 100 may automatically control the braking,acceleration, and/or steering of vehicle 200 (e.g., by sending controlsignals to one or more of throttling system 220, braking system 230, andsteering system 240). Further, system 100 may analyze the collected dataand issue warnings and/or alerts to vehicle occupants based on theanalysis of the collected data. Additional details regarding the variousembodiments that are provided by system 100 are provided below.

Forward-Facing Multi-Imaging System

As discussed above, system 100 may provide drive assist functionalitythat uses a multi-camera system. The multi-camera system may use one ormore cameras facing in the forward direction of a vehicle. In otherembodiments, the multi-camera system may include one or more camerasfacing to the side of a vehicle or to the rear of the vehicle. In oneembodiment, for example, system 100 may use a two-camera imaging system,where a first camera and a second camera (e.g., image capture devices122 and 124) may be positioned at the front and/or the sides of avehicle (e.g., vehicle 200). The first camera may have a field of viewthat is greater than, less than, or partially overlapping with, thefield of view of the second camera. In addition, the first camera may beconnected to a first image processor to perform monocular image analysisof images provided by the first camera, and the second camera may beconnected to a second image processor to perform monocular imageanalysis of images provided by the second camera. The outputs (e.g.,processed information) of the first and second image processors may becombined. In some embodiments, the second image processor may receiveimages from both the first camera and second camera to perform stereoanalysis. In another embodiment, system 100 may use a three-cameraimaging system where each of the cameras has a different field of view.Such a system may, therefore, make decisions based on informationderived from objects located at varying distances both forward and tothe sides of the vehicle. References to monocular image analysis mayrefer to instances where image analysis is performed based on imagescaptured from a single point of view (e.g., from a single camera).Stereo image analysis may refer to instances where image analysis isperformed based on two or more images captured with one or morevariations of an image capture parameter. For example, captured imagessuitable for performing stereo image analysis may include imagescaptured: from two or more different positions, from different fields ofview, using different focal lengths, along with parallax information,etc.

For example, in one embodiment, system 100 may implement a three cameraconfiguration using image capture devices 122, 124, and 126. In such aconfiguration, image capture device 122 may provide a narrow field ofview (e.g., 34 degrees, or other values selected from a range of about20 to 45 degrees, etc.), image capture device 124 may provide a widefield of view (e.g., 150 degrees or other values selected from a rangeof about 100 to about 180 degrees), and image capture device 126 mayprovide an intermediate field of view (e.g., 46 degrees or other valuesselected from a range of about 35 to about 60 degrees). In someembodiments, image capture device 126 may act as a main or primarycamera. Image capture devices 122, 124, and 126 may be positioned behindrearview mirror 310 and positioned substantially side-by-side (e.g., 6cm apart). Further, in some embodiments, as discussed above, one or moreof image capture devices 122, 124, and 126 may be mounted behind glareshield 380 that is flush with the windshield of vehicle 200. Suchshielding may act to minimize the impact of any reflections from insidethe car on image capture devices 122, 124, and 126.

In another embodiment, as discussed above in connection with FIGS. 3Band 3C, the wide field of view camera (e.g., image capture device 124 inthe above example) may be mounted lower than the narrow and main fieldof view cameras (e.g., image devices 122 and 126 in the above example).This configuration may provide a free line of sight from the wide fieldof view camera. To reduce reflections, the cameras may be mounted closeto the windshield of vehicle 200, and may include polarizers on thecameras to damp reflected light.

A three camera system may provide certain performance characteristics.For example, some embodiments may include an ability to validate thedetection of objects by one camera based on detection results fromanother camera. In the three camera configuration discussed above,processing unit 110 may include, for example, three processing devices(e.g., three EyeQ series of processor chips, as discussed above), witheach processing device dedicated to processing images captured by one ormore of image capture devices 122, 124, and 126.

In a three camera system, a first processing device may receive imagesfrom both the main camera and the narrow field of view camera, andperform vision processing of the narrow FOV camera to, for example,detect other vehicles, pedestrians, lane marks, traffic signs, trafficlights, and other road objects. Further, the first processing device maycalculate a disparity of pixels between the images from the main cameraand the narrow camera and create a 3D reconstruction of the environmentof vehicle 200. The first processing device may then combine the 3Dreconstruction with 3D map data or with 3D information calculated basedon information from another camera.

The second processing device may receive images from main camera andperform vision processing to detect other vehicles, pedestrians, lanemarks, traffic signs, traffic lights, and other road objects.Additionally, the second processing device may calculate a cameradisplacement and, based on the displacement, calculate a disparity ofpixels between successive images and create a 3D reconstruction of thescene (e.g., a structure from motion). The second processing device maysend the structure from motion based 3D reconstruction to the firstprocessing device to be combined with the stereo 3D images.

The third processing device may receive images from the wide FOV cameraand process the images to detect vehicles, pedestrians, lane marks,traffic signs, traffic lights, and other road objects. The thirdprocessing device may further execute additional processing instructionsto analyze images to identify objects moving in the image, such asvehicles changing lanes, pedestrians, etc.

In some embodiments, having streams of image-based information capturedand processed independently may provide an opportunity for providingredundancy in the system. Such redundancy may include, for example,using a first image capture device and the images processed from thatdevice to validate and/or supplement information obtained by capturingand processing image information from at least a second image capturedevice.

In some embodiments, system 100 may use two image capture devices (e.g.,image capture devices 122 and 124) in providing navigation assistancefor vehicle 200 and use a third image capture device (e.g., imagecapture device 126) to provide redundancy and validate the analysis ofdata received from the other two image capture devices. For example, insuch a configuration, image capture devices 122 and 124 may provideimages for stereo analysis by system 100 for navigating vehicle 200,while image capture device 126 may provide images for monocular analysisby system 100 to provide redundancy and validation of informationobtained based on images captured from image capture device 122 and/orimage capture device 124. That is, image capture device 126 (and acorresponding processing device) may be considered to provide aredundant sub-system for providing a check on the analysis derived fromimage capture devices 122 and 124 (e.g., to provide an automaticemergency braking (AEB) system). Furthermore, in some embodiments,redundancy and validation of received data may be supplemented based oninformation received from one more sensors (e.g., radar, lidar, acousticsensors, information received from one or more transceivers outside of avehicle, etc.).

One of skill in the art will recognize that the above cameraconfigurations, camera placements, number of cameras, camera locations,etc., are examples only. These components and others described relativeto the overall system may be assembled and used in a variety ofdifferent configurations without departing from the scope of thedisclosed embodiments. Further details regarding usage of a multi-camerasystem to provide driver assist and/or autonomous vehicle functionalityfollow below.

FIG. 4 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 4, memory 140 may store a monocular image analysismodule 402, a stereo image analysis module 404, a velocity andacceleration module 406, and a navigational response module 408. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140. Further, application processor 180 and/or image processor190 may execute the instructions stored in any of modules 402, 404, 406,and 408 included in memory 140. One of skill in the art will understandthat references in the following discussions to processing unit 110 mayrefer to application processor 180 and image processor 190 individuallyor collectively. Accordingly, steps of any of the following processesmay be performed by one or more processing devices.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar, lidar, etc.) to perform the monocular image analysis. Asdescribed in connection with FIGS. 5A-5D below, monocular image analysismodule 402 may include instructions for detecting a set of featureswithin the set of images, such as lane markings, vehicles, pedestrians,road signs, highway exit ramps, traffic lights, hazardous objects, andany other feature associated with an environment of a vehicle. Based onthe analysis, system 100 (e.g., via processing unit 110) may cause oneor more navigational responses in vehicle 200, such as a turn, a laneshift, a change in acceleration, and the like, as discussed below inconnection with navigational response module 408.

In one embodiment, stereo image analysis module 404 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs stereo image analysis of first and secondsets of images acquired by a combination of image capture devicesselected from any of image capture devices 122, 124, and 126. In someembodiments, processing unit 110 may combine information from the firstand second sets of images with additional sensory information (e.g.,information from radar) to perform the stereo image analysis. Forexample, stereo image analysis module 404 may include instructions forperforming stereo image analysis based on a first set of images acquiredby image capture device 124 and a second set of images acquired by imagecapture device 126. As described in connection with FIG. 6 below, stereoimage analysis module 404 may include instructions for detecting a setof features within the first and second sets of images, such as lanemarkings, vehicles, pedestrians, road signs, highway exit ramps, trafficlights, hazardous objects, and the like. Based on the analysis,processing unit 110 may cause one or more navigational responses invehicle 200, such as a turn, a lane shift, a change in acceleration, andthe like, as discussed below in connection with navigational responsemodule 408. Furthermore, in some embodiments, stereo image analysismodule 404 may implement techniques associated with a trained system(such as a neural network or a deep neural network) or an untrainedsystem, such as a system that may be configured to use computer visionalgorithms to detect and/or label objects in an environment from whichsensory information was captured and processed. In one embodiment,stereo image analysis module 404 and/or other image processing modulesmay be configured to use a combination of a trained and untrainedsystem.

In one embodiment, velocity and acceleration module 406 may storesoftware configured to analyze data received from one or more computingand electromechanical devices in vehicle 200 that are configured tocause a change in velocity and/or acceleration of vehicle 200. Forexample, processing unit 110 may execute instructions associated withvelocity and acceleration module 406 to calculate a target speed forvehicle 200 based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include, for example, a target position, velocity, and/oracceleration, the position and/or speed of vehicle 200 relative to anearby vehicle, pedestrian, or road object, position information forvehicle 200 relative to lane markings of the road, and the like. Inaddition, processing unit 110 may calculate a target speed for vehicle200 based on sensory input (e.g., information from radar) and input fromother systems of vehicle 200, such as throttling system 220, brakingsystem 230, and/or steering system 240 of vehicle 200. Based on thecalculated target speed, processing unit 110 may transmit electronicsignals to throttling system 220, braking system 230, and/or steeringsystem 240 of vehicle 200 to trigger a change in velocity and/oracceleration by, for example, physically depressing the brake or easingup off the accelerator of vehicle 200.

In one embodiment, navigational response module 408 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include position and speed information associated with nearbyvehicles, pedestrians, and road objects, target position information forvehicle 200, and the like. Additionally, in some embodiments, thenavigational response may be based (partially or fully) on map data, apredetermined position of vehicle 200, and/or a relative velocity or arelative acceleration between vehicle 200 and one or more objectsdetected from execution of monocular image analysis module 402 and/orstereo image analysis module 404. Navigational response module 408 mayalso determine a desired navigational response based on sensory input(e.g., information from radar) and inputs from other systems of vehicle200, such as throttling system 220, braking system 230, and steeringsystem 240 of vehicle 200. Based on the desired navigational response,processing unit 110 may transmit electronic signals to throttling system220, braking system 230, and steering system 240 of vehicle 200 totrigger a desired navigational response by, for example, turning thesteering wheel of vehicle 200 to achieve a rotation of a predeterminedangle. In some embodiments, processing unit 110 may use the output ofnavigational response module 408 (e.g., the desired navigationalresponse) as an input to execution of velocity and acceleration module406 for calculating a change in speed of vehicle 200.

Furthermore, any of the modules (e.g., modules 402, 404, and 406)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 5A is a flowchart showing an exemplary process 500A for causing oneor more navigational responses based on monocular image analysis,consistent with disclosed embodiments. At step 510, processing unit 110may receive a plurality of images via data interface 128 betweenprocessing unit 110 and image acquisition unit 120. For instance, acamera included in image acquisition unit 120 (such as image capturedevice 122 having field of view 202) may capture a plurality of imagesof an area forward of vehicle 200 (or to the sides or rear of a vehicle,for example) and transmit them over a data connection (e.g., digital,wired, USB, wireless, Bluetooth, etc.) to processing unit 110.Processing unit 110 may execute monocular image analysis module 402 toanalyze the plurality of images at step 520, as described in furtherdetail in connection with FIGS. 5B-5D below. By performing the analysis,processing unit 110 may detect a set of features within the set ofimages, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, and the like.

Processing unit 110 may also execute monocular image analysis module 402to detect various road hazards at step 520, such as, for example, partsof a truck tire, fallen road signs, loose cargo, small animals, and thelike. Road hazards may vary in structure, shape, size, and color, whichmay make detection of such hazards more challenging. In someembodiments, processing unit 110 may execute monocular image analysismodule 402 to perform multi-frame analysis on the plurality of images todetect road hazards. For example, processing unit 110 may estimatecamera motion between consecutive image frames and calculate thedisparities in pixels between the frames to construct a 3D-map of theroad. Processing unit 110 may then use the 3D-map to detect the roadsurface, as well as hazards existing above the road surface.

At step 530, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 520 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration,and the like. In some embodiments, processing unit 110 may use dataderived from execution of velocity and acceleration module 406 to causethe one or more navigational responses. Additionally, multiplenavigational responses may occur simultaneously, in sequence, or anycombination thereof. For instance, processing unit 110 may cause vehicle200 to shift one lane over and then accelerate by, for example,sequentially transmitting control signals to steering system 240 andthrottling system 220 of vehicle 200. Alternatively, processing unit 110may cause vehicle 200 to brake while at the same time shifting lanes by,for example, simultaneously transmitting control signals to brakingsystem 230 and steering system 240 of vehicle 200.

FIG. 5B is a flowchart showing an exemplary process 500B for detectingone or more vehicles and/or pedestrians in a set of images, consistentwith disclosed embodiments. Processing unit 110 may execute monocularimage analysis module 402 to implement process 500B. At step 540,processing unit 110 may determine a set of candidate objectsrepresenting possible vehicles and/or pedestrians. For example,processing unit 110 may scan one or more images, compare the images toone or more predetermined patterns, and identify within each imagepossible locations that may contain objects of interest (e.g., vehicles,pedestrians, or portions thereof). The predetermined patterns may bedesigned in such a way to achieve a high rate of “false hits” and a lowrate of “misses.” For example, processing unit 110 may use a lowthreshold of similarity to predetermined patterns for identifyingcandidate objects as possible vehicles or pedestrians. Doing so mayallow processing unit 110 to reduce the probability of missing (e.g.,not identifying) a candidate object representing a vehicle orpedestrian.

At step 542, processing unit 110 may filter the set of candidate objectsto exclude certain candidates (e.g., irrelevant or less relevantobjects) based on classification criteria. Such criteria may be derivedfrom various properties associated with object types stored in adatabase (e.g., a database stored in memory 140). Properties may includeobject shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Thus, processing unit 110 may use one or more setsof criteria to reject false candidates from the set of candidateobjects.

At step 544, processing unit 110 may analyze multiple frames of imagesto determine whether objects in the set of candidate objects representvehicles and/or pedestrians. For example, processing unit 110 may tracka detected candidate object across consecutive frames and accumulateframe-by-frame data associated with the detected object (e.g., size,position relative to vehicle 200, etc.). Additionally, processing unit110 may estimate parameters for the detected object and compare theobject's frame-by-frame position data to a predicted position.

At step 546, processing unit 110 may construct a set of measurements forthe detected objects. Such measurements may include, for example,position, velocity, and acceleration values (relative to vehicle 200)associated with the detected objects. In some embodiments, processingunit 110 may construct the measurements based on estimation techniquesusing a series of time-based observations such as Kalman filters orlinear quadratic estimation (LQE), and/or based on available modelingdata for different object types (e.g., cars, trucks, pedestrians,bicycles, road signs, etc.). The Kalman filters may be based on ameasurement of an object's scale, where the scale measurement isproportional to a time to collision (e.g., the amount of time forvehicle 200 to reach the object). Thus, by performing steps 540-546,processing unit 110 may identify vehicles and pedestrians appearingwithin the set of captured images and derive information (e.g.,position, speed, size) associated with the vehicles and pedestrians.Based on the identification and the derived information, processing unit110 may cause one or more navigational responses in vehicle 200, asdescribed in connection with FIG. 5A, above.

At step 548, processing unit 110 may perform an optical flow analysis ofone or more images to reduce the probabilities of detecting a “falsehit” and missing a candidate object that represents a vehicle orpedestrian. The optical flow analysis may refer to, for example,analyzing motion patterns relative to vehicle 200 in the one or moreimages associated with other vehicles and pedestrians, and that aredistinct from road surface motion. Processing unit 110 may calculate themotion of candidate objects by observing the different positions of theobjects across multiple image frames, which are captured at differenttimes. Processing unit 110 may use the position and time values asinputs into mathematical models for calculating the motion of thecandidate objects. Thus, optical flow analysis may provide anothermethod of detecting vehicles and pedestrians that are nearby vehicle200. Processing unit 110 may perform optical flow analysis incombination with steps 540-546 to provide redundancy for detectingvehicles and pedestrians and increase the reliability of system 100.

FIG. 5C is a flowchart showing an exemplary process 500C for detectingroad marks and/or lane geometry information in a set of images,consistent with disclosed embodiments. Processing unit 110 may executemonocular image analysis module 402 to implement process 500C. At step550, processing unit 110 may detect a set of objects by scanning one ormore images. To detect segments of lane markings, lane geometryinformation, and other pertinent road marks, processing unit 110 mayfilter the set of objects to exclude those determined to be irrelevant(e.g., minor potholes, small rocks, etc.). At step 552, processing unit110 may group together the segments detected in step 550 belonging tothe same road mark or lane mark. Based on the grouping, processing unit110 may develop a model to represent the detected segments, such as amathematical model.

At step 554, processing unit 110 may construct a set of measurementsassociated with the detected segments. In some embodiments, processingunit 110 may create a projection of the detected segments from the imageplane onto the real-world plane. The projection may be characterizedusing a 3rd-degree polynomial having coefficients corresponding tophysical properties such as the position, slope, curvature, andcurvature derivative of the detected road. In generating the projection,processing unit 110 may take into account changes in the road surface,as well as pitch and roll rates associated with vehicle 200. Inaddition, processing unit 110 may model the road elevation by analyzingposition and motion cues present on the road surface. Further,processing unit 110 may estimate the pitch and roll rates associatedwith vehicle 200 by tracking a set of feature points in the one or moreimages.

At step 556, processing unit 110 may perform multi-frame analysis by,for example, tracking the detected segments across consecutive imageframes and accumulating frame-by-frame data associated with detectedsegments. As processing unit 110 performs multi-frame analysis, the setof measurements constructed at step 554 may become more reliable andassociated with an increasingly higher confidence level. Thus, byperforming steps 550, 552, 554, and 556, processing unit 110 mayidentify road marks appearing within the set of captured images andderive lane geometry information. Based on the identification and thederived information, processing unit 110 may cause one or morenavigational responses in vehicle 200, as described in connection withFIG. 5A, above.

At step 558, processing unit 110 may consider additional sources ofinformation to further develop a safety model for vehicle 200 in thecontext of its surroundings. Processing unit 110 may use the safetymodel to define a context in which system 100 may execute autonomouscontrol of vehicle 200 in a safe manner. To develop the safety model, insome embodiments, processing unit 110 may consider the position andmotion of other vehicles, the detected road edges and barriers, and/orgeneral road shape descriptions extracted from map data (such as datafrom map database 160). By considering additional sources ofinformation, processing unit 110 may provide redundancy for detectingroad marks and lane geometry and increase the reliability of system 100.

FIG. 5D is a flowchart showing an exemplary process 500D for detectingtraffic lights in a set of images, consistent with disclosedembodiments. Processing unit 110 may execute monocular image analysismodule 402 to implement process 500D. At step 560, processing unit 110may scan the set of images and identify objects appearing at locationsin the images likely to contain traffic lights. For example, processingunit 110 may filter the identified objects to construct a set ofcandidate objects, excluding those objects unlikely to correspond totraffic lights. The filtering may be done based on various propertiesassociated with traffic lights, such as shape, dimensions, texture,position (e.g., relative to vehicle 200), and the like. Such propertiesmay be based on multiple examples of traffic lights and traffic controlsignals and stored in a database. In some embodiments, processing unit110 may perform multi-frame analysis on the set of candidate objectsreflecting possible traffic lights. For example, processing unit 110 maytrack the candidate objects across consecutive image frames, estimatethe real-world position of the candidate objects, and filter out thoseobjects that are moving (which are unlikely to be traffic lights). Insome embodiments, processing unit 110 may perform color analysis on thecandidate objects and identify the relative position of the detectedcolors appearing inside possible traffic lights.

At step 562, processing unit 110 may analyze the geometry of a junction.The analysis may be based on any combination of: (i) the number of lanesdetected on either side of vehicle 200, (ii) markings (such as arrowmarks) detected on the road, and (iii) descriptions of the junctionextracted from map data (such as data from map database 160). Processingunit 110 may conduct the analysis using information derived fromexecution of monocular analysis module 402. In addition, Processing unit110 may determine a correspondence between the traffic lights detectedat step 560 and the lanes appearing near vehicle 200.

As vehicle 200 approaches the junction, at step 564, processing unit 110may update the confidence level associated with the analyzed junctiongeometry and the detected traffic lights. For instance, the number oftraffic lights estimated to appear at the junction as compared with thenumber actually appearing at the junction may impact the confidencelevel. Thus, based on the confidence level, processing unit 110 maydelegate control to the driver of vehicle 200 in order to improve safetyconditions. By performing steps 560, 562, and 564, processing unit 110may identify traffic lights appearing within the set of captured imagesand analyze junction geometry information. Based on the identificationand the analysis, processing unit 110 may cause one or more navigationalresponses in vehicle 200, as described in connection with FIG. 5A,above.

FIG. 5E is a flowchart showing an exemplary process 500E for causing oneor more navigational responses in vehicle 200 based on a vehicle path,consistent with the disclosed embodiments. At step 570, processing unit110 may construct an initial vehicle path associated with vehicle 200.The vehicle path may be represented using a set of points expressed incoordinates (x, z), and the distance d_(i) between two points in the setof points may fall in the range of 1 to 5 meters. In one embodiment,processing unit 110 may construct the initial vehicle path using twopolynomials, such as left and right road polynomials. Processing unit110 may calculate the geometric midpoint between the two polynomials andoffset each point included in the resultant vehicle path by apredetermined offset (e.g., a smart lane offset), if any (an offset ofzero may correspond to travel in the middle of a lane). The offset maybe in a direction perpendicular to a segment between any two points inthe vehicle path. In another embodiment, processing unit 110 may use onepolynomial and an estimated lane width to offset each point of thevehicle path by half the estimated lane width plus a predeterminedoffset (e.g., a smart lane offset).

At step 572, processing unit 110 may update the vehicle path constructedat step 570. Processing unit 110 may reconstruct the vehicle pathconstructed at step 570 using a higher resolution, such that thedistance d_(k) between two points in the set of points representing thevehicle path is less than the distance d_(i) described above. Forexample, the distance d_(k) may fall in the range of 0.1 to 0.3 meters.Processing unit 110 may reconstruct the vehicle path using a parabolicspline algorithm, which may yield a cumulative distance vector Scorresponding to the total length of the vehicle path (i.e., based onthe set of points representing the vehicle path).

At step 574, processing unit 110 may determine a look-ahead point(expressed in coordinates as (x_(l), z_(l))) based on the updatedvehicle path constructed at step 572. Processing unit 110 may extractthe look-ahead point from the cumulative distance vector S, and thelook-ahead point may be associated with a look-ahead distance andlook-ahead time. The look-ahead distance, which may have a lower boundranging from 10 to 20 meters, may be calculated as the product of thespeed of vehicle 200 and the look-ahead time. For example, as the speedof vehicle 200 decreases, the look-ahead distance may also decrease(e.g., until it reaches the lower bound). The look-ahead time, which mayrange from 0.5 to 1.5 seconds, may be inversely proportional to the gainof one or more control loops associated with causing a navigationalresponse in vehicle 200, such as the heading error tracking controlloop. For example, the gain of the heading error tracking control loopmay depend on the bandwidth of a yaw rate loop, a steering actuatorloop, car lateral dynamics, and the like. Thus, the higher the gain ofthe heading error tracking control loop, the lower the look-ahead time.

At step 576, processing unit 110 may determine a heading error and yawrate command based on the look-ahead point determined at step 574.Processing unit 110 may determine the heading error by calculating thearctangent of the look-ahead point, e.g., arctan (x_(l)/z_(l)).Processing unit 110 may determine the yaw rate command as the product ofthe heading error and a high-level control gain. The high-level controlgain may be equal to: (2/look-ahead time), if the look-ahead distance isnot at the lower bound. Otherwise, the high-level control gain may beequal to: (2*speed of vehicle 200/look-ahead distance).

FIG. 5F is a flowchart showing an exemplary process 500F for determiningwhether a leading vehicle is changing lanes, consistent with thedisclosed embodiments. At step 580, processing unit 110 may determinenavigation information associated with a leading vehicle (e.g., avehicle traveling ahead of vehicle 200). For example, processing unit110 may determine the position, velocity (e.g., direction and speed),and/or acceleration of the leading vehicle, using the techniquesdescribed in connection with FIGS. 5A and 5B, above. Processing unit 110may also determine one or more road polynomials, a look-ahead point(associated with vehicle 200), and/or a snail trail (e.g., a set ofpoints describing a path taken by the leading vehicle), using thetechniques described in connection with FIG. 5E, above.

At step 582, processing unit 110 may analyze the navigation informationdetermined at step 580. In one embodiment, processing unit 110 maycalculate the distance between a snail trail and a road polynomial(e.g., along the trail). If the variance of this distance along thetrail exceeds a predetermined threshold (for example, 0.1 to 0.2 meterson a straight road, 0.3 to 0.4 meters on a moderately curvy road, and0.5 to 0.6 meters on a road with sharp curves), processing unit 110 maydetermine that the leading vehicle is likely changing lanes. In the casewhere multiple vehicles are detected traveling ahead of vehicle 200,processing unit 110 may compare the snail trails associated with eachvehicle. Based on the comparison, processing unit 110 may determine thata vehicle whose snail trail does not match with the snail trails of theother vehicles is likely changing lanes. Processing unit 110 mayadditionally compare the curvature of the snail trail (associated withthe leading vehicle) with the expected curvature of the road segment inwhich the leading vehicle is traveling. The expected curvature may beextracted from map data (e.g., data from map database 160), from roadpolynomials, from other vehicles' snail trails, from prior knowledgeabout the road, and the like. If the difference in curvature of thesnail trail and the expected curvature of the road segment exceeds apredetermined threshold, processing unit 110 may determine that theleading vehicle is likely changing lanes.

In another embodiment, processing unit 110 may compare the leadingvehicle's instantaneous position with the look-ahead point (associatedwith vehicle 200) over a specific period of time (e.g., 0.5 to 1.5seconds). If the distance between the leading vehicle's instantaneousposition and the look-ahead point varies during the specific period oftime, and the cumulative sum of variation exceeds a predeterminedthreshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8meters on a moderately curvy road, and 1.3 to 1.7 meters on a road withsharp curves), processing unit 110 may determine that the leadingvehicle is likely changing lanes. In another embodiment, processing unit110 may analyze the geometry of the snail trail by comparing the lateraldistance traveled along the trail with the expected curvature of thesnail trail. The expected radius of curvature may be determinedaccording to the calculation: (δ_(z) ²+δ_(x) ²)/2/(δ_(x)), where δ_(x)represents the lateral distance traveled and δ_(z) represents thelongitudinal distance traveled. If the difference between the lateraldistance traveled and the expected curvature exceeds a predeterminedthreshold (e.g., 500 to 700 meters), processing unit 110 may determinethat the leading vehicle is likely changing lanes. In anotherembodiment, processing unit 110 may analyze the position of the leadingvehicle. If the position of the leading vehicle obscures a roadpolynomial (e.g., the leading vehicle is overlaid on top of the roadpolynomial), then processing unit 110 may determine that the leadingvehicle is likely changing lanes. In the case where the position of theleading vehicle is such that, another vehicle is detected ahead of theleading vehicle and the snail trails of the two vehicles are notparallel, processing unit 110 may determine that the (closer) leadingvehicle is likely changing lanes.

At step 584, processing unit 110 may determine whether or not leadingvehicle 200 is changing lanes based on the analysis performed at step582. For example, processing unit 110 may make the determination basedon a weighted average of the individual analyses performed at step 582.Under such a scheme, for example, a decision by processing unit 110 thatthe leading vehicle is likely changing lanes based on a particular typeof analysis may be assigned a value of “1” (and “0” to represent adetermination that the leading vehicle is not likely changing lanes).Different analyses performed at step 582 may be assigned differentweights, and the disclosed embodiments are not limited to any particularcombination of analyses and weights.

FIG. 6 is a flowchart showing an exemplary process 600 for causing oneor more navigational responses based on stereo image analysis,consistent with disclosed embodiments. At step 610, processing unit 110may receive a first and second plurality of images via data interface128. For example, cameras included in image acquisition unit 120 (suchas image capture devices 122 and 124 having fields of view 202 and 204)may capture a first and second plurality of images of an area forward ofvehicle 200 and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc.) to processing unit 110. In some embodiments,processing unit 110 may receive the first and second plurality of imagesvia two or more data interfaces. The disclosed embodiments are notlimited to any particular data interface configurations or protocols.

At step 620, processing unit 110 may execute stereo image analysismodule 404 to perform stereo image analysis of the first and secondplurality of images to create a 3D map of the road in front of thevehicle and detect features within the images, such as lane markings,vehicles, pedestrians, road signs, highway exit ramps, traffic lights,road hazards, and the like. Stereo image analysis may be performed in amanner similar to the steps described in connection with FIGS. 5A-5D,above. For example, processing unit 110 may execute stereo imageanalysis module 404 to detect candidate objects (e.g., vehicles,pedestrians, road marks, traffic lights, road hazards, etc.) within thefirst and second plurality of images, filter out a subset of thecandidate objects based on various criteria, and perform multi-frameanalysis, construct measurements, and determine a confidence level forthe remaining candidate objects. In performing the steps above,processing unit 110 may consider information from both the first andsecond plurality of images, rather than information from one set ofimages alone. For example, processing unit 110 may analyze thedifferences in pixel-level data (or other data subsets from among thetwo streams of captured images) for a candidate object appearing in boththe first and second plurality of images. As another example, processingunit 110 may estimate a position and/or velocity of a candidate object(e.g., relative to vehicle 200) by observing that the object appears inone of the plurality of images but not the other or relative to otherdifferences that may exist relative to objects appearing if the twoimage streams. For example, position, velocity, and/or accelerationrelative to vehicle 200 may be determined based on trajectories,positions, movement characteristics, etc. of features associated with anobject appearing in one or both of the image streams.

At step 630, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 620 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration, achange in velocity, braking, and the like. In some embodiments,processing unit 110 may use data derived from execution of velocity andacceleration module 406 to cause the one or more navigational responses.Additionally, multiple navigational responses may occur simultaneously,in sequence, or any combination thereof.

FIG. 7 is a flowchart showing an exemplary process 700 for causing oneor more navigational responses based on an analysis of three sets ofimages, consistent with disclosed embodiments. At step 710, processingunit 110 may receive a first, second, and third plurality of images viadata interface 128. For instance, cameras included in image acquisitionunit 120 (such as image capture devices 122, 124, and 126 having fieldsof view 202, 204, and 206) may capture a first, second, and thirdplurality of images of an area forward and/or to the side of vehicle 200and transmit them over a digital connection (e.g., USB, wireless,Bluetooth, etc.) to processing unit 110. In some embodiments, processingunit 110 may receive the first, second, and third plurality of imagesvia three or more data interfaces. For example, each of image capturedevices 122, 124, 126 may have an associated data interface forcommunicating data to processing unit 110. The disclosed embodiments arenot limited to any particular data interface configurations orprotocols.

At step 720, processing unit 110 may analyze the first, second, andthird plurality of images to detect features within the images, such aslane markings, vehicles, pedestrians, road signs, highway exit ramps,traffic lights, road hazards, and the like. The analysis may beperformed in a manner similar to the steps described in connection withFIGS. 5A-5D and 6, above. For instance, processing unit 110 may performmonocular image analysis (e.g., via execution of monocular imageanalysis module 402 and based on the steps described in connection withFIGS. 5A-5D, above) on each of the first, second, and third plurality ofimages. Alternatively, processing unit 110 may perform stereo imageanalysis (e.g., via execution of stereo image analysis module 404 andbased on the steps described in connection with FIG. 6, above) on thefirst and second plurality of images, the second and third plurality ofimages, and/or the first and third plurality of images. The processedinformation corresponding to the analysis of the first, second, and/orthird plurality of images may be combined. In some embodiments,processing unit 110 may perform a combination of monocular and stereoimage analyses. For example, processing unit 110 may perform monocularimage analysis (e.g., via execution of monocular image analysis module402) on the first plurality of images and stereo image analysis (e.g.,via execution of stereo image analysis module 404) on the second andthird plurality of images. The configuration of image capture devices122, 124, and 126—including their respective locations and fields ofview 202, 204, and 206—may influence the types of analyses conducted onthe first, second, and third plurality of images. The disclosedembodiments are not limited to a particular configuration of imagecapture devices 122, 124, and 126, or the types of analyses conducted onthe first, second, and third plurality of images.

In some embodiments, processing unit 110 may perform testing on system100 based on the images acquired and analyzed at steps 710 and 720. Suchtesting may provide an indicator of the overall performance of system100 for certain configurations of image capture devices 122, 124, and126. For example, processing unit 110 may determine the proportion of“false hits” (e.g., cases where system 100 incorrectly determined thepresence of a vehicle or pedestrian) and “misses.”

At step 730, processing unit 110 may cause one or more navigationalresponses in vehicle 200 based on information derived from two of thefirst, second, and third plurality of images. Selection of two of thefirst, second, and third plurality of images may depend on variousfactors, such as, for example, the number, types, and sizes of objectsdetected in each of the plurality of images. Processing unit 110 mayalso make the selection based on image quality and resolution, theeffective field of view reflected in the images, the number of capturedframes, the extent to which one or more objects of interest actuallyappear in the frames (e.g., the percentage of frames in which an objectappears, the proportion of the object that appears in each such frame,etc.), and the like.

In some embodiments, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images bydetermining the extent to which information derived from one imagesource is consistent with information derived from other image sources.For example, processing unit 110 may combine the processed informationderived from each of image capture devices 122, 124, and 126 (whether bymonocular analysis, stereo analysis, or any combination of the two) anddetermine visual indicators (e.g., lane markings, a detected vehicle andits location and/or path, a detected traffic light, etc.) that areconsistent across the images captured from each of image capture devices122, 124, and 126. Processing unit 110 may also exclude information thatis inconsistent across the captured images (e.g., a vehicle changinglanes, a lane model indicating a vehicle that is too close to vehicle200, etc.). Thus, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images based onthe determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift, achange in acceleration, and the like. Processing unit 110 may cause theone or more navigational responses based on the analysis performed atstep 720 and the techniques as described above in connection with FIG.4. Processing unit 110 may also use data derived from execution ofvelocity and acceleration module 406 to cause the one or morenavigational responses. In some embodiments, processing unit 110 maycause the one or more navigational responses based on a relativeposition, relative velocity, and/or relative acceleration betweenvehicle 200 and an object detected within any of the first, second, andthird plurality of images. Multiple navigational responses may occursimultaneously, in sequence, or any combination thereof.

Analysis of captured images may allow for the generation and use of asparse map model for autonomous vehicle navigation. In addition,analysis of captured images may allow for the localization of anautonomous vehicle using identified lane markings. Embodiments fordetection of particular characteristics based on one or more particularanalyses of captured images and for navigation of an autonomous vehicleusing a sparse map model will be discussed below with reference to FIGS.8-28.

Sparse Road Model for Autonomous Vehicle Navigation

In some embodiments, the disclosed systems and methods may use a sparsemap for autonomous vehicle navigation. In particular, the sparse map maybe for autonomous vehicle navigation along a road segment. For example,the sparse map may provide sufficient information for navigating anautonomous vehicle without storing and/or updating a large quantity ofdata. As discussed below in further detail, an autonomous vehicle mayuse the sparse map to navigate one or more roads based on one or morestored trajectories.

Sparse Map for Autonomous Vehicle Navigation

In some embodiments, the disclosed systems and methods may generate asparse map for autonomous vehicle navigation. For example, the sparsemap may provide sufficient information for navigation without requiringexcessive data storage or data transfer rates. As discussed below infurther detail, a vehicle (which may be an autonomous vehicle) may usethe sparse map to navigate one or more roads. For example, in someembodiments, the sparse map may include data related to a road andpotentially landmarks along the road that may be sufficient for vehiclenavigation, but which also exhibit small data footprints. For example,the sparse data maps described in detail below may require significantlyless storage space and data transfer bandwidth as compared with digitalmaps including detailed map information, such as image data collectedalong a road.

For example, rather than storing detailed representations of a roadsegment, the sparse data map may store three-dimensional polynomialrepresentations of preferred vehicle paths along a road. These paths mayrequire very little data storage space. Further, in the described sparsedata maps, landmarks may be identified and included in the sparse maproad model to aid in navigation. These landmarks may be located at anyspacing suitable for enabling vehicle navigation, but in some cases,such landmarks need not be identified and included in the model at highdensities and short spacings. Rather, in some cases, navigation may bepossible based on landmarks that are spaced apart by at least 50 meters,at least 100 meters, at least 500 meters, at least 1 kilometer, or atleast 2 kilometers. As will be discussed in more detail in othersections, the sparse map may be generated based on data collected ormeasured by vehicles equipped with various sensors and devices, such asimage capture devices, Global Positioning System sensors, motionsensors, etc., as the vehicles travel along roadways. In some cases, thesparse map may be generated based on data collected during multipledrives of one or more vehicles along a particular roadway. Generating asparse map using multiple drives of one or more vehicles may be referredto as “crowdsourcing” a sparse map.

Consistent with disclosed embodiments, an autonomous vehicle system mayuse a sparse map for navigation. For example, the disclosed systems andmethods may distribute a sparse map for generating a road navigationmodel for an autonomous vehicle and may navigate an autonomous vehiclealong a road segment using a sparse map and/or a generated roadnavigation model. Sparse maps consistent with the present disclosure mayinclude one or more three-dimensional contours that may representpredetermined trajectories that autonomous vehicles may traverse as theymove along associated road segments.

Sparse maps consistent with the present disclosure may also include datarepresenting one or more road features. Such road features may includerecognized landmarks, road signature profiles, and any otherroad-related features useful in navigating a vehicle. Sparse mapsconsistent with the present disclosure may enable autonomous navigationof a vehicle based on relatively small amounts of data included in thesparse map. For example, rather than including detailed representationsof a road, such as road edges, road curvature, images associated withroad segments, or data detailing other physical features associated witha road segment, the disclosed embodiments of the sparse map may requirerelatively little storage space (and relatively little bandwidth whenportions of the sparse map are transferred to a vehicle) but may stilladequately provide for autonomous vehicle navigation. The small datafootprint of the disclosed sparse maps, discussed in further detailbelow, may be achieved in some embodiments by storing representations ofroad-related elements that require small amounts of data but stillenable autonomous navigation.

For example, rather than storing detailed representations of variousaspects of a road, the disclosed sparse maps may store polynomialrepresentations of one or more trajectories that a vehicle may followalong the road. Thus, rather than storing (or having to transfer)details regarding the physical nature of the road to enable navigationalong the road, using the disclosed sparse maps, a vehicle may benavigated along a particular road segment without, in some cases, havingto interpret physical aspects of the road, but rather, by aligning itspath of travel with a trajectory (e.g., a polynomial spline) along theparticular road segment. In this way, the vehicle may be navigated basedmainly upon the stored trajectory (e.g., a polynomial spline) that mayrequire much less storage space than an approach involving storage ofroadway images, road parameters, road layout, etc.

In addition to the stored polynomial representations of trajectoriesalong a road segment, the disclosed sparse maps may also include smalldata objects that may represent a road feature. In some embodiments, thesmall data objects may include digital signatures, which are derivedfrom a digital image (or a digital signal) that was obtained by a sensor(e.g., a camera or other sensor, such as a suspension sensor) onboard avehicle traveling along the road segment. The digital signature may havea reduced size relative to the signal that was acquired by the sensor.In some embodiments, the digital signature may be created to becompatible with a classifier function that is configured to detect andto identify the road feature from the signal that is acquired by thesensor, for example, during a subsequent drive. In some embodiments, adigital signature may be created such that the digital signature has afootprint that is as small as possible, while retaining the ability tocorrelate or match the road feature with the stored signature based onan image (or a digital signal generated by a sensor, if the storedsignature is not based on an image and/or includes other data) of theroad feature that is captured by a camera onboard a vehicle travelingalong the same road segment at a subsequent time.

In some embodiments, a size of the data objects may be furtherassociated with a uniqueness of the road feature. For example, for aroad feature that is detectable by a camera onboard a vehicle, and wherethe camera system onboard the vehicle is coupled to a classifier that iscapable of distinguishing the image data corresponding to that roadfeature as being associated with a particular type of road feature, forexample, a road sign, and where such a road sign is locally unique inthat area (e.g., there is no identical road sign or road sign of thesame type nearby), it may be sufficient to store data indicating thetype of the road feature and its location.

As will be discussed in further detail below, road features (e.g.,landmarks along a road segment) may be stored as small data objects thatmay represent a road feature in relatively few bytes, while at the sametime providing sufficient information for recognizing and using such afeature for navigation. In one example, a road sign may be identified asa recognized landmark on which navigation of a vehicle may be based. Arepresentation of the road sign may be stored in the sparse map toinclude, e.g., a few bytes of data indicating a type of landmark (e.g.,a stop sign) and a few bytes of data indicating a location of thelandmark (e.g., coordinates). Navigating based on such data-lightrepresentations of the landmarks (e.g., using representations sufficientfor locating, recognizing, and navigating based upon the landmarks) mayprovide a desired level of navigational functionality associated withsparse maps without significantly increasing the data overheadassociated with the sparse maps. This lean representation of landmarks(and other road features) may take advantage of the sensors andprocessors included onboard such vehicles that are configured to detect,identify, and/or classify certain road features.

When, for example, a sign or even a particular type of a sign is locallyunique (e.g., when there is no other sign or no other sign of the sametype) in a given area, the sparse map may use data indicating a type ofa landmark (a sign or a specific type of sign), and during navigation(e.g., autonomous navigation) when a camera onboard an autonomousvehicle captures an image of the area including a sign (or of a specifictype of sign), the processor may process the image, detect the sign (ifindeed present in the image), classify the image as a sign (or as aspecific type of sign), and correlate the location of the image with thelocation of the sign as stored in the sparse map.

Generating a Sparse Map

In some embodiments, a sparse map may include at least one linerepresentation of a road surface feature extending along a road segmentand a plurality of landmarks associated with the road segment. Incertain aspects, the sparse map may be generated via “crowdsourcing,”for example, through image analysis of a plurality of images acquired asone or more vehicles traverse the road segment.

FIG. 8 shows a sparse map 800 that one or more vehicles, e.g., vehicle200 (which may be an autonomous vehicle), may access for providingautonomous vehicle navigation. Sparse map 800 may be stored in a memory,such as memory 140 or 150. Such memory devices may include any types ofnon-transitory storage devices or computer-readable media. For example,in some embodiments, memory 140 or 150 may include hard drives, compactdiscs, flash memory, magnetic based memory devices, optical based memorydevices, etc. In some embodiments, sparse map 800 may be stored in adatabase (e.g., map database 160) that may be stored in memory 140 or150, or other types of storage devices.

In some embodiments, sparse map 800 may be stored on a storage device ora non-transitory computer-readable medium provided onboard vehicle 200(e.g., a storage device included in a navigation system onboard vehicle200). A processor (e.g., processing unit 110) provided on vehicle 200may access sparse map 800 stored in the storage device orcomputer-readable medium provided onboard vehicle 200 in order togenerate navigational instructions for guiding the autonomous vehicle200 as the vehicle traverses a road segment.

Sparse map 800 need not be stored locally with respect to a vehicle,however. In some embodiments, sparse map 800 may be stored on a storagedevice or computer-readable medium provided on a remote server thatcommunicates with vehicle 200 or a device associated with vehicle 200. Aprocessor (e.g., processing unit 110) provided on vehicle 200 mayreceive data included in sparse map 800 from the remove server and mayexecute the data for guiding the autonomous driving of vehicle 200. Insuch embodiments, the remote server may store all of sparse map 800 oronly a portion thereof. Accordingly, the storage device orcomputer-readable medium provided onboard vehicle 200 and/or onboard oneor more additional vehicles may store the remaining portion(s) of sparsemap 800.

Furthermore, in such embodiments, sparse map 800 may be made accessibleto a plurality of vehicles traversing various road segments (e.g., tens,hundreds, thousands, or millions of vehicles, etc.). It should be notedalso that sparse map 800 may include multiple sub-maps. For example, insome embodiments, sparse map 800 may include hundreds, thousands,millions, or more, of sub-maps that may be used in navigating a vehicle.Such sub-maps may be referred to as local maps, and a vehicle travelingalong a roadway may access any number of local maps relevant to alocation in which the vehicle is traveling. The local map sections ofsparse map 800 may be stored with a Global Navigation Satellite System(GNSS) key as an index to the database of sparse map 800. Thus, whilecomputation of steering angles for navigating a host vehicle in thepresent system may be performed without reliance upon a GNSS position ofthe host vehicle, road features, or landmarks, such GNSS information maybe used for retrieval of relevant local maps.

Collection of data and generation of sparse map 800 is covered ingreater detail below, for example, with respect to FIG. 19. In general,however, sparse map 800 may be generated based on data collected fromone or more vehicles as they travel along roadways. For example, usingsensors aboard the one or more vehicles (e.g., cameras, speedometers,GPS, accelerometers, etc.), the trajectories that the one or morevehicles travel along a roadway may be recorded, and the polynomialrepresentation of a preferred trajectory for vehicles making subsequenttrips along the roadway may be determined based on the collectedtrajectories travelled by the one or more vehicles. Similarly, datacollected by the one or more vehicles may aid in identifying potentiallandmarks along a particular roadway. Data collected from traversingvehicles may also be used to identify road profile information, such asroad width profiles, road roughness profiles, traffic line spacingprofiles, road conditions, etc. Using the collected information, sparsemap 800 may be generated and distributed (e.g., for local storage or viaon-the-fly data transmission) for use in navigating one or moreautonomous vehicles. However, in some embodiments, map generation maynot end upon initial generation of the map. As will be discussed ingreater detail below, sparse map 800 may be continuously or periodicallyupdated based on data collected from vehicles as those vehicles continueto traverse roadways included in sparse map 800.

Data recorded in sparse map 800 may include position information basedon Global Positioning System (GPS) data. For example, locationinformation may be included in sparse map 800 for various map elements,including, for example, landmark locations, road profile locations, etc.Locations for map elements included in sparse map 800 may be obtainedusing GPS data collected from vehicles traversing a roadway. Forexample, a vehicle passing an identified landmark may determine alocation of the identified landmark using GPS position informationassociated with the vehicle and a determination of a location of theidentified landmark relative to the vehicle (e.g., based on imageanalysis of data collected from one or more cameras on board thevehicle). Such location determinations of an identified landmark (or anyother feature included in sparse map 800) may be repeated as additionalvehicles pass the location of the identified landmark. Some or all ofthe additional location determinations may be used to refine thelocation information stored in sparse map 800 relative to the identifiedlandmark. For example, in some embodiments, multiple positionmeasurements relative to a particular feature stored in sparse map 800may be averaged together. Any other mathematical operations, however,may also be used to refine a stored location of a map element based on aplurality of determined locations for the map element.

The sparse map of the disclosed embodiments may enable autonomousnavigation of a vehicle using relatively small amounts of stored data.In some embodiments, sparse map 800 may have a data density (e.g.,including data representing the target trajectories, landmarks, and anyother stored road features) of less than 2 MB per kilometer of roads,less than 1 MB per kilometer of roads, less than 500 kB per kilometer ofroads, or less than 100 kB per kilometer of roads. In some embodiments,the data density of sparse map 800 may be less than 10 kB per kilometerof roads or even less than 2 kB per kilometer of roads (e.g., 1.6 kB perkilometer), or no more than 10 kB per kilometer of roads, or no morethan 20 kB per kilometer of roads. In some embodiments, most, if notall, of the roadways of the United States may be navigated autonomouslyusing a sparse map having a total of 4 GB or less of data. These datadensity values may represent an average over an entire sparse map 800,over a local map within sparse map 800, and/or over a particular roadsegment within sparse map 800.

As noted, sparse map 800 may include representations of a plurality oftarget trajectories 810 for guiding autonomous driving or navigationalong a road segment. Such target trajectories may be stored asthree-dimensional splines. The target trajectories stored in sparse map800 may be determined based on two or more reconstructed trajectories ofprior traversals of vehicles along a particular road segment, forexample, as discussed with respect to FIG. 29. A road segment may beassociated with a single target trajectory or multiple targettrajectories. For example, on a two lane road, a first target trajectorymay be stored to represent an intended path of travel along the road ina first direction, and a second target trajectory may be stored torepresent an intended path of travel along the road in another direction(e.g., opposite to the first direction). Additional target trajectoriesmay be stored with respect to a particular road segment. For example, ona multi-lane road one or more target trajectories may be storedrepresenting intended paths of travel for vehicles in one or more lanesassociated with the multi-lane road. In some embodiments, each lane of amulti-lane road may be associated with its own target trajectory. Inother embodiments, there may be fewer target trajectories stored thanlanes present on a multi-lane road. In such cases, a vehicle navigatingthe multi-lane road may use any of the stored target trajectories toguides its navigation by taking into account an amount of lane offsetfrom a lane for which a target trajectory is stored (e.g., if a vehicleis traveling in the left most lane of a three lane highway, and a targettrajectory is stored only for the middle lane of the highway, thevehicle may navigate using the target trajectory of the middle lane byaccounting for the amount of lane offset between the middle lane and theleft-most lane when generating navigational instructions).

In some embodiments, the target trajectory may represent an ideal paththat a vehicle should take as the vehicle travels. The target trajectorymay be located, for example, at an approximate center of a lane oftravel. In other cases, the target trajectory may be located elsewhererelative to a road segment. For example, a target trajectory mayapproximately coincide with a center of a road, an edge of a road, or anedge of a lane, etc. In such cases, navigation based on the targettrajectory may include a determined amount of offset to be maintainedrelative to the location of the target trajectory. Moreover, in someembodiments, the determined amount of offset to be maintained relativeto the location of the target trajectory may differ based on a type ofvehicle (e.g., a passenger vehicle including two axles may have adifferent offset from a truck including more than two axles along atleast a portion of the target trajectory).

Sparse map 800 may also include data relating to a plurality ofpredetermined landmarks 820 associated with particular road segments,local maps, etc. As discussed in greater detail below, these landmarksmay be used in navigation of the autonomous vehicle. For example, insome embodiments, the landmarks may be used to determine a currentposition of the vehicle relative to a stored target trajectory. Withthis position information, the autonomous vehicle may be able to adjusta heading direction to match a direction of the target trajectory at thedetermined location.

The plurality of landmarks 820 may be identified and stored in sparsemap 800 at any suitable spacing. In some embodiments, landmarks may bestored at relatively high densities (e.g., every few meters or more). Insome embodiments, however, significantly larger landmark spacing valuesmay be employed. For example, in sparse map 800, identified (orrecognized) landmarks may be spaced apart by 10 meters, 20 meters, 50meters, 100 meters, 1 kilometer, or 2 kilometers. In some cases, theidentified landmarks may be located at distances of even more than 2kilometers apart.

Between landmarks, and therefore between determinations of vehicleposition relative to a target trajectory, the vehicle may navigate basedon dead reckoning in which the vehicle uses sensors to determine its egomotion and estimate its position relative to the target trajectory.Because errors may accumulate during navigation by dead reckoning, overtime the position determinations relative to the target trajectory maybecome increasingly less accurate. The vehicle may use landmarksoccurring in sparse map 800 (and their known locations) to remove thedead reckoning-induced errors in position determination. In this way,the identified landmarks included in sparse map 800 may serve asnavigational anchors from which an accurate position of the vehiclerelative to a target trajectory may be determined. Because a certainamount of error may be acceptable in position location, an identifiedlandmark need not always be available to an autonomous vehicle. Rather,suitable navigation may be possible even based on landmark spacings, asnoted above, of 10 meters, 20 meters, 50 meters, 100 meters, 500 meters,1 kilometer, 2 kilometers, or more. In some embodiments, a density of 1identified landmark every 1 km of road may be sufficient to maintain alongitudinal position determination accuracy within 1 m. Thus, not everypotential landmark appearing along a road segment need be stored insparse map 800.

Moreover, in some embodiments, lane markings may be used forlocalization of the vehicle during landmark spacings. By using lanemarkings during landmark spacings, the accumulation of during navigationby dead reckoning may be minimized. In particular, such localization isdiscussed below with respect to FIG. 35.

In addition to target trajectories and identified landmarks, sparse map800 may include information relating to various other road features. Forexample, FIG. 9A illustrates a representation of curves along aparticular road segment that may be stored in sparse map 800. In someembodiments, a single lane of a road may be modeled by athree-dimensional polynomial description of left and right sides of theroad. Such polynomials representing left and right sides of a singlelane are shown in FIG. 9A. Regardless of how many lanes a road may have,the road may be represented using polynomials in a way similar to thatillustrated in FIG. 9A. For example, left and right sides of amulti-lane road may be represented by polynomials similar to those shownin FIG. 9A, and intermediate lane markings included on a multi-lane road(e.g., dashed markings representing lane boundaries, solid yellow linesrepresenting boundaries between lanes traveling in different directions,etc.) may also be represented using polynomials such as those shown inFIG. 9A.

As shown in FIG. 9A, a lane 900 may be represented using polynomials(e.g., a first order, second order, third order, or any suitable orderpolynomials). For illustration, lane 900 is shown as a two-dimensionallane and the polynomials are shown as two-dimensional polynomials. Asdepicted in FIG. 9A, lane 900 includes a left side 910 and a right side920. In some embodiments, more than one polynomial may be used torepresent a location of each side of the road or lane boundary. Forexample, each of left side 910 and right side 920 may be represented bya plurality of polynomials of any suitable length. In some cases, thepolynomials may have a length of about 100 m, although other lengthsgreater than or less than 100 m may also be used. Additionally, thepolynomials can overlap with one another in order to facilitate seamlesstransitions in navigating based on subsequently encountered polynomialsas a host vehicle travels along a roadway. For example, each of leftside 910 and right side 920 may be represented by a plurality of thirdorder polynomials separated into segments of about 100 meters in length(an example of the first predetermined range), and overlapping eachother by about 50 meters. The polynomials representing the left side 910and the right side 920 may or may not have the same order. For example,in some embodiments, some polynomials may be second order polynomials,some may be third order polynomials, and some may be fourth orderpolynomials.

In the example shown in FIG. 9A, left side 910 of lane 900 isrepresented by two groups of third order polynomials. The first groupincludes polynomial segments 911, 912, and 913. The second groupincludes polynomial segments 914, 915, and 916. The two groups, whilesubstantially parallel to each other, follow the locations of theirrespective sides of the road. Polynomial segments 911, 912, 913, 914,915, and 916 have a length of about 100 meters and overlap adjacentsegments in the series by about 50 meters. As noted previously, however,polynomials of different lengths and different overlap amounts may alsobe used. For example, the polynomials may have lengths of 500 m, 1 km,or more, and the overlap amount may vary from 0 to 50 m, 50 m to 100 m,or greater than 100 m. Additionally, while FIG. 9A is shown asrepresenting polynomials extending in 2D space (e.g., on the surface ofthe paper), it is to be understood that these polynomials may representcurves extending in three dimensions (e.g., including a heightcomponent) to represent elevation changes in a road segment in additionto X-Y curvature. In the example shown in FIG. 9A, right side 920 oflane 900 is further represented by a first group having polynomialsegments 921, 922, and 923 and a second group having polynomial segments924, 925, and 926.

Returning to the target trajectories of sparse map 800, FIG. 9B shows athree-dimensional polynomial representing a target trajectory for avehicle traveling along a particular road segment. The target trajectoryrepresents not only the X-Y path that a host vehicle should travel alonga particular road segment, but also the elevation change that the hostvehicle will experience when traveling along the road segment. Thus,each target trajectory in sparse map 800 may be represented by one ormore three-dimensional polynomials, like the three-dimensionalpolynomial 950 shown in FIG. 9B. Sparse map 800 may include a pluralityof trajectories (e.g., millions or billions or more to representtrajectories of vehicles along various road segments along roadwaysthroughout the world). In some embodiments, each target trajectory maycorrespond to a spline connecting three-dimensional polynomial segments.

Regarding the data footprint of polynomial curves stored in sparse map800, in some embodiments, each third degree polynomial may berepresented by four parameters, each requiring four bytes of data.Suitable representations may be obtained with third degree polynomialsrequiring about 192 bytes of data for every 100 m. This may translate toapproximately 200 kB per hour in data usage/transfer requirements for ahost vehicle traveling approximately 100 km/hr.

Sparse map 800 may describe the lanes network using a combination ofgeometry descriptors and meta-data. The geometry may be described bypolynomials or splines as described above. The meta-data may describethe number of lanes, special characteristics (such as a car pool lane),and possibly other sparse labels. The total footprint of such indicatorsmay be negligible.

Accordingly, a sparse map according to embodiments of the presentdisclosure may include at least one line representation of a roadsurface feature extending along the road segment, each linerepresentation representing a path along the road segment substantiallycorresponding with the road surface feature. In some embodiments, asdiscussed above, the at least one line representation of the roadsurface feature may include a spline, a polynomial representation, or acurve. Furthermore, in some embodiments, the road surface feature mayinclude at least one of a road edge or a lane marking. Moreover, asdiscussed below with respect to “crowdsourcing,” the road surfacefeature may be identified through image analysis of a plurality ofimages acquired as one or more vehicles traverse the road segment.

As previously noted, sparse map 800 may include a plurality ofpredetermined landmarks associated with a road segment. Rather thanstoring actual images of the landmarks and relying, for example, onimage recognition analysis based on captured images and stored images,each landmark in sparse map 800 may be represented and recognized usingless data than a stored, actual image would require. Data representinglandmarks may still include sufficient information for describing oridentifying the landmarks along a road. Storing data describingcharacteristics of landmarks, rather than the actual images oflandmarks, may reduce the size of sparse map 800.

FIG. 10 illustrates examples of types of landmarks that may berepresented in sparse map 800. The landmarks may include any visible andidentifiable objects along a road segment. The landmarks may be selectedsuch that they are fixed and do not change often with respect to theirlocations and/or content. The landmarks included in sparse map 800 maybe useful in determining a location of vehicle 200 with respect to atarget trajectory as the vehicle traverses a particular road segment.Examples of landmarks may include traffic signs, directional signs,general signs (e.g., rectangular signs), roadside fixtures (e.g.,lampposts, reflectors, etc.), and any other suitable category. In someembodiments, lane marks on the road, may also be included as landmarksin sparse map 800.

Examples of landmarks shown in FIG. 10 include traffic signs,directional signs, roadside fixtures, and general signs. Traffic signsmay include, for example, speed limit signs (e.g., speed limit sign1000), yield signs (e.g., yield sign 1005), route number signs (e.g.,route number sign 1010), traffic light signs (e.g., traffic light sign1015), stop signs (e.g., stop sign 1020). Directional signs may includea sign that includes one or more arrows indicating one or moredirections to different places. For example, directional signs mayinclude a highway sign 1025 having arrows for directing vehicles todifferent roads or places, an exit sign 1030 having an arrow directingvehicles off a road, etc. Accordingly, at least one of the plurality oflandmarks may include a road sign.

General signs may be unrelated to traffic. For example, general signsmay include billboards used for advertisement, or a welcome boardadjacent a border between two countries, states, counties, cities, ortowns. FIG. 10 shows a general sign 1040 (“Joe's Restaurant”). Althoughgeneral sign 1040 may have a rectangular shape, as shown in FIG. 10,general sign 1040 may have other shapes, such as square, circle,triangle, etc.

Landmarks may also include roadside fixtures. Roadside fixtures may beobjects that are not signs, and may not be related to traffic ordirections. For example, roadside fixtures may include lampposts (e.g.,lamppost 1035), power line posts, traffic light posts, etc.

Landmarks may also include beacons that may be specifically designed forusage in an autonomous vehicle navigation system. For example, suchbeacons may include stand-alone structures placed at predeterminedintervals to aid in navigating a host vehicle. Such beacons may alsoinclude visual/graphical information added to existing road signs (e.g.,icons, emblems, bar codes, etc.) that may be identified or recognized bya vehicle traveling along a road segment. Such beacons may also includeelectronic components. In such embodiments, electronic beacons (e.g.,RFID tags, etc.) may be used to transmit non-visual information to ahost vehicle. Such information may include, for example, landmarkidentification and/or landmark location information that a host vehiclemay use in determining its position along a target trajectory.

In some embodiments, the landmarks included in sparse map 800 may berepresented by a data object of a predetermined size. The datarepresenting a landmark may include any suitable parameters foridentifying a particular landmark. For example, in some embodiments,landmarks stored in sparse map 800 may include parameters such as aphysical size of the landmark (e.g., to support estimation of distanceto the landmark based on a known size/scale), a distance to a previouslandmark, lateral offset, height, a type code (e.g., a landmarktype—what type of directional sign, traffic sign, etc.), a GPScoordinate (e.g., to support global localization), and any othersuitable parameters. Each parameter may be associated with a data size.For example, a landmark size may be stored using 8 bytes of data. Adistance to a previous landmark, a lateral offset, and height may bespecified using 12 bytes of data. A type code associated with a landmarksuch as a directional sign or a traffic sign may require about 2 bytesof data. For general signs, an image signature enabling identificationof the general sign may be stored using 50 bytes of data storage. Thelandmark GPS position may be associated with 16 bytes of data storage.These data sizes for each parameter are examples only, and other datasizes may also be used.

Representing landmarks in sparse map 800 in this manner may offer a leansolution for efficiently representing landmarks in the database. In someembodiments, signs may be referred to as semantic signs and non-semanticsigns. A semantic sign may include any class of signs for which there'sa standardized meaning (e.g., speed limit signs, warning signs,directional signs, etc.). A non-semantic sign may include any sign thatis not associated with a standardized meaning (e.g., general advertisingsigns, signs identifying business establishments, etc.). For example,each semantic sign may be represented with 38 bytes of data (e.g., 8bytes for size; 12 bytes for distance to previous landmark, lateraloffset, and height; 2 bytes for a type code; and 16 bytes for GPScoordinates). Sparse map 800 may use a tag system to represent landmarktypes. In some cases, each traffic sign or directional sign may beassociated with its own tag, which may be stored in the database as partof the landmark identification. For example, the database may include onthe order of 1000 different tags to represent various traffic signs andon the order of about 10000 different tags to represent directionalsigns. Of course, any suitable number of tags may be used, andadditional tags may be created as needed. General purpose signs may berepresented in some embodiments using less than about 100 bytes (e.g.,about 86 bytes including 8 bytes for size; 12 bytes for distance toprevious landmark, lateral offset, and height; 50 bytes for an imagesignature; and 16 bytes for GPS coordinates).

Thus, for semantic road signs not requiring an image signature, the datadensity impact to sparse map 800, even at relatively high landmarkdensities of about 1 per 50 m, may be on the order of about 760 bytesper kilometer (e.g., 20 landmarks per km×38 bytes per landmark=760bytes). Even for general purpose signs including an image signaturecomponent, the data density impact is about 1.72 kB per km (e.g., 20landmarks per km×86 bytes per landmark=1,720 bytes). For semantic roadsigns, this equates to about 76 kB per hour of data usage for a vehicletraveling 100 km/hr. For general purpose signs, this equates to about170 kB per hour for a vehicle traveling 100 km/hr.

In some embodiments, a generally rectangular object, such as arectangular sign, may be represented in sparse map 800 by no more than100 byte of data. The representation of the generally rectangular object(e.g., general sign 1040) in sparse map 800 may include a condensedimage signature (e.g., condensed image signature 1045) associated withthe generally rectangular object. This condensed image signature may beused, for example, to aid in identification of a general purpose sign,for example, as a recognized landmark. Such a condensed image signature(e.g., image information derived from actual image data representing anobject) may avoid a need for storage of an actual image of an object ora need for comparative image analysis performed on actual images inorder to recognize landmarks.

Referring to FIG. 10, sparse map 800 may include or store a condensedimage signature 1045 associated with a general sign 1040, rather than anactual image of general sign 1040. For example, after an image capturedevice (e.g., image capture device 122, 124, or 126) captures an imageof general sign 1040, a processor (e.g., image processor 190 or anyother processor that can process images either aboard or remotelylocated relative to a host vehicle) may perform an image analysis toextract/create condensed image signature 1045 that includes a uniquesignature or pattern associated with general sign 1040. In oneembodiment, condensed image signature 1045 may include a shape, colorpattern, a brightness pattern, or any other feature that may beextracted from the image of general sign 1040 for describing generalsign 1040.

For example, in FIG. 10, the circles, triangles, and stars shown incondensed image signature 1045 may represent areas of different colors.The pattern represented by the circles, triangles, and stars may bestored in sparse map 800, e.g., within the 50 bytes designated toinclude an image signature. Notably, the circles, triangles, and starsare not necessarily meant to indicate that such shapes are stored aspart of the image signature. Rather, these shapes are meant toconceptually represent recognizable areas having discernible colordifferences, textual areas, graphical shapes, or other variations incharacteristics that may be associated with a general purpose sign. Suchcondensed image signatures can be used to identify a landmark in theform of a general sign. For example, the condensed image signature canbe used to perform a same-not-same analysis based on a comparison of astored condensed image signature with image data captured, for example,using a camera onboard an autonomous vehicle.

Accordingly, the plurality of landmarks may be identified through imageanalysis of the plurality of images acquired as one or more vehiclestraverse the road segment. As explained below with respect to“crowdsourcing,” in some embodiments, the image analysis to identify theplurality of landmarks may include accepting potential landmarks when aratio of images in which the landmark does appear to images in which thelandmark does not appear exceeds a threshold. Furthermore, in someembodiments, the image analysis to identify the plurality of landmarksmay include rejecting potential landmarks when a ratio of images inwhich the landmark does not appear to images in which the landmark doesappear exceeds a threshold.

Returning to the target trajectories a host vehicle may use to navigatea particular road segment, FIG. 11A shows polynomial representationstrajectories capturing during a process of building or maintainingsparse map 800. A polynomial representation of a target trajectoryincluded in sparse map 800 may be determined based on two or morereconstructed trajectories of prior traversals of vehicles along thesame road segment. In some embodiments, the polynomial representation ofthe target trajectory included in sparse map 800 may be an aggregationof two or more reconstructed trajectories of prior traversals ofvehicles along the same road segment. In some embodiments, thepolynomial representation of the target trajectory included in sparsemap 800 may be an average of the two or more reconstructed trajectoriesof prior traversals of vehicles along the same road segment. Othermathematical operations may also be used to construct a targettrajectory along a road path based on reconstructed trajectoriescollected from vehicles traversing along a road segment.

As shown in FIG. 11A, a road segment 1100 may be travelled by a numberof vehicles 200 at different times. Each vehicle 200 may collect datarelating to a path that the vehicle took along the road segment. Thepath traveled by a particular vehicle may be determined based on cameradata, accelerometer information, speed sensor information, and/or GPSinformation, among other potential sources. Such data may be used toreconstruct trajectories of vehicles traveling along the road segment,and based on these reconstructed trajectories, a target trajectory (ormultiple target trajectories) may be determined for the particular roadsegment. Such target trajectories may represent a preferred path of ahost vehicle (e.g., guided by an autonomous navigation system) as thevehicle travels along the road segment.

In the example shown in FIG. 11A, a first reconstructed trajectory 1101may be determined based on data received from a first vehicle traversingroad segment 1100 at a first time period (e.g., day 1), a secondreconstructed trajectory 1102 may be obtained from a second vehicletraversing road segment 1100 at a second time period (e.g., day 2), anda third reconstructed trajectory 1103 may be obtained from a thirdvehicle traversing road segment 1100 at a third time period (e.g., day3). Each trajectory 1101, 1102, and 1103 may be represented by apolynomial, such as a three-dimensional polynomial. It should be notedthat in some embodiments, any of the reconstructed trajectories may beassembled onboard the vehicles traversing road segment 1100.

Additionally, or alternatively, such reconstructed trajectories may bedetermined on a server side based on information received from vehiclestraversing road segment 1100. For example, in some embodiments, vehicles200 may transmit data to one or more servers relating to their motionalong road segment 1100 (e.g., steering angle, heading, time, position,speed, sensed road geometry, and/or sensed landmarks, among things). Theserver may reconstruct trajectories for vehicles 200 based on thereceived data. The server may also generate a target trajectory forguiding navigation of autonomous vehicle that will travel along the sameroad segment 1100 at a later time based on the first, second, and thirdtrajectories 1101, 1102, and 1103. While a target trajectory may beassociated with a single prior traversal of a road segment, in someembodiments, each target trajectory included in sparse map 800 may bedetermined based on two or more reconstructed trajectories of vehiclestraversing the same road segment. In FIG. 11A, the target trajectory isrepresented by 1110. In some embodiments, the target trajectory 1110 maybe generated based on an average of the first, second, and thirdtrajectories 1101, 1102, and 1103. In some embodiments, the targettrajectory 1110 included in sparse map 800 may be an aggregation (e.g.,a weighted combination) of two or more reconstructed trajectories.Aligning drive data to construct trajectories is further discussed belowwith respect to FIG. 29.

FIGS. 11B and 11C further illustrate the concept of target trajectoriesassociated with road segments present within a geographic region 1111.As shown in FIG. 11B, a first road segment 1120 within geographic region1111 may include a multilane road, which includes two lanes 1122designated for vehicle travel in a first direction and two additionallanes 1124 designated for vehicle travel in a second direction oppositeto the first direction. Lanes 1122 and lanes 1124 may be separated by adouble yellow line 1123. Geographic region 1111 may also include abranching road segment 1130 that intersects with road segment 1120. Roadsegment 1130 may include a two-lane road, each lane being designated fora different direction of travel. Geographic region 1111 may also includeother road features, such as a stop line 1132, a stop sign 1134, a speedlimit sign 1136, and a hazard sign 1138.

As shown in FIG. 11C, sparse map 800 may include a local map 1140including a road model for assisting with autonomous navigation ofvehicles within geographic region 1111. For example, local map 1140 mayinclude target trajectories for one or more lanes associated with roadsegments 1120 and/or 1130 within geographic region 1111. For example,local map 1140 may include target trajectories 1141 and/or 1142 that anautonomous vehicle may access or rely upon when traversing lanes 1122.Similarly, local map 1140 may include target trajectories 1143 and/or1144 that an autonomous vehicle may access or rely upon when traversinglanes 1124. Further, local map 1140 may include target trajectories 1145and/or 1146 that an autonomous vehicle may access or rely upon whentraversing road segment 1130. Target trajectory 1147 represents apreferred path an autonomous vehicle should follow when transitioningfrom lanes 1120 (and specifically, relative to target trajectory 1141associated with a right-most lane of lanes 1120) to road segment 1130(and specifically, relative to a target trajectory 1145 associated witha first side of road segment 1130. Similarly, target trajectory 1148represents a preferred path an autonomous vehicle should follow whentransitioning from road segment 1130 (and specifically, relative totarget trajectory 1146) to a portion of road segment 1124 (andspecifically, as shown, relative to a target trajectory 1143 associatedwith a left lane of lanes 1124.

Sparse map 800 may also include representations of other road-relatedfeatures associated with geographic region 1111. For example, sparse map800 may also include representations of one or more landmarks identifiedin geographic region 1111. Such landmarks may include a first landmark1150 associated with stop line 1132, a second landmark 1152 associatedwith stop sign 1134, a third landmark associated with speed limit sign1154, and a fourth landmark 1156 associated with hazard sign 1138. Suchlandmarks may be used, for example, to assist an autonomous vehicle indetermining its current location relative to any of the shown targettrajectories, such that the vehicle may adjust its heading to match adirection of the target trajectory at the determined location.Navigating using landmarks from a sparse map is further discussed belowwith respect to FIG. 26.

In some embodiments, sparse may 800 may also include road signatureprofiles. Such road signature profiles may be associated with anydiscernible/measurable variation in at least one parameter associatedwith a road. For example, in some cases, such profiles may be associatedwith variations in road surface information such as variations insurface roughness of a particular road segment, variations in road widthover a particular road segment, variations in distances between dashedlines painted along a particular road segment, variations in roadcurvature along a particular road segment, etc. FIG. 11D shows anexample of a road signature profile 1160. While profile 1160 mayrepresent any of the parameters mentioned above, or others, in oneexample, profile 1160 may represent a measure of road surface roughness,as obtained, for example, by monitoring one or more sensors providingoutputs indicative of an amount of suspension displacement as a vehicletravels a particular road segment.

Alternatively or concurrently, profile 1160 may represent variation inroad width, as determined based on image data obtained via a cameraonboard a vehicle traveling a particular road segment. Such profiles maybe useful, for example, in determining a particular location of anautonomous vehicle relative to a particular target trajectory. That is,as it traverses a road segment, an autonomous vehicle may measure aprofile associated with one or more parameters associated with the roadsegment. If the measured profile can be correlated/matched with apredetermined profile that plots the parameter variation with respect toposition along the road segment, then the measured and predeterminedprofiles may be used (e.g., by overlaying corresponding sections of themeasured and predetermined profiles) in order to determine a currentposition along the road segment and, therefore, a current positionrelative to a target trajectory for the road segment.

In some embodiments, sparse map 800 may include different trajectoriesbased on different characteristics associated with a user of autonomousvehicles, environmental conditions, and/or other parameters relating todriving. For example, in some embodiments, different trajectories may begenerated based on different user preferences and/or profiles. Sparsemap 800 including such different trajectories may be provided todifferent autonomous vehicles of different users. For example, someusers may prefer to avoid toll roads, while others may prefer to takethe shortest or fastest routes, regardless of whether there is a tollroad on the route. The disclosed systems may generate different sparsemaps with different trajectories based on such different userpreferences or profiles. As another example, some users may prefer totravel in a fast moving lane, while others may prefer to maintain aposition in the central lane at all times.

Different trajectories may be generated and included in sparse map 800based on different environmental conditions, such as day and night,snow, rain, fog, etc. Autonomous vehicles driving under differentenvironmental conditions may be provided with sparse map 800 generatedbased on such different environmental conditions. In some embodiments,cameras provided on autonomous vehicles may detect the environmentalconditions, and may provide such information back to a server thatgenerates and provides sparse maps. For example, the server may generateor update an already generated sparse map 800 to include trajectoriesthat may be more suitable or safer for autonomous driving under thedetected environmental conditions. The update of sparse map 800 based onenvironmental conditions may be performed dynamically as the autonomousvehicles are traveling along roads.

Other different parameters relating to driving may also be used as abasis for generating and providing different sparse maps to differentautonomous vehicles. For example, when an autonomous vehicle istraveling at a high speed, turns may be tighter. Trajectories associatedwith specific lanes, rather than roads, may be included in sparse map800 such that the autonomous vehicle may maintain within a specific laneas the vehicle follows a specific trajectory. When an image captured bya camera onboard the autonomous vehicle indicates that the vehicle hasdrifted outside of the lane (e.g., crossed the lane mark), an action maybe triggered within the vehicle to bring the vehicle back to thedesignated lane according to the specific trajectory.

Crowdsourcing a Sparse Map

In some embodiments, the disclosed systems and methods may generate asparse for autonomous vehicle navigation. For example, disclosed systemsand methods may use crowdsourced data for generation of a sparse thatone or more autonomous vehicles may use to navigate along a system ofroads. As used herein, “crowdsourcing” means that data are received fromvarious vehicles (e.g., autonomous vehicles) travelling on a roadsegment at different times, and such data are used to generate and/orupdate the road model. The model may, in turn, be transmitted to thevehicles or other vehicles later travelling along the road segment forassisting autonomous vehicle navigation. The road model may include aplurality of target trajectories representing preferred trajectoriesthat autonomous vehicles should follow as they traverse a road segment.The target trajectories may be the same as a reconstructed actualtrajectory collected from a vehicle traversing a road segment, which maybe transmitted from the vehicle to a server. In some embodiments, thetarget trajectories may be different from actual trajectories that oneor more vehicles previously took when traversing a road segment. Thetarget trajectories may be generated based on actual trajectories (e.g.,through averaging or any other suitable operation). An example ofalignment of crowdsourced data for generating target trajectories isdiscussed below with respect to FIG. 29.

The vehicle trajectory data that a vehicle may upload to a server maycorrespond with the actual reconstructed trajectory for the vehicle ormay correspond to a recommended trajectory, which may be based on orrelated to the actual reconstructed trajectory of the vehicle, but maydiffer from the actual reconstructed trajectory. For example, vehiclesmay modify their actual, reconstructed trajectories and submit (e.g.,recommend) to the server the modified actual trajectories. The roadmodel may use the recommended, modified trajectories as targettrajectories for autonomous navigation of other vehicles.

In addition to trajectory information, other information for potentialuse in building a sparse data map 800 may include information relatingto potential landmark candidates. For example, through crowd sourcing ofinformation, the disclosed systems and methods may identify potentiallandmarks in an environment and refine landmark positions. The landmarksmay be used by a navigation system of autonomous vehicles to determineand/or adjust the position of the vehicle along the target trajectories.

The reconstructed trajectories that a vehicle may generate as thevehicle travels along a road may be obtained by any suitable method. Insome embodiments, the reconstructed trajectories may be developed bystitching together segments of motion for the vehicle, using, e.g., egomotion estimation (e.g., three dimensional translation and threedimensional rotation of the camera, and hence the body of the vehicle).The rotation and translation estimation may be determined based onanalysis of images captured by one or more image capture devices alongwith information from other sensors or devices, such as inertial sensorsand speed sensors. For example, the inertial sensors may include anaccelerometer or other suitable sensors configured to measure changes intranslation and/or rotation of the vehicle body. The vehicle may includea speed sensor that measures a speed of the vehicle.

In some embodiments, the ego motion of the camera (and hence the vehiclebody) may be estimated based on an optical flow analysis of the capturedimages. An optical flow analysis of a sequence of images identifiesmovement of pixels from the sequence of images, and based on theidentified movement, determines motions of the vehicle. The ego motionmay be integrated over time and along the road segment to reconstruct atrajectory associated with the road segment that the vehicle hasfollowed.

Data (e.g., reconstructed trajectories) collected by multiple vehiclesin multiple drives along a road segment at different times may be usedto construct the road model (e.g., including the target trajectories,etc.) included in sparse data map 800. Data collected by multiplevehicles in multiple drives along a road segment at different times mayalso be averaged to increase an accuracy of the model. In someembodiments, data regarding the road geometry and/or landmarks may bereceived from multiple vehicles that travel through the common roadsegment at different times. Such data received from different vehiclesmay be combined to generate the road model and/or to update the roadmodel.

The geometry of a reconstructed trajectory (and also a targettrajectory) along a road segment may be represented by a curve in threedimensional space, which may be a spline connecting three dimensionalpolynomials. The reconstructed trajectory curve may be determined fromanalysis of a video stream or a plurality of images captured by a camerainstalled on the vehicle. In some embodiments, a location is identifiedin each frame or image that is a few meters ahead of the currentposition of the vehicle. This location is where the vehicle is expectedto travel to in a predetermined time period. This operation may berepeated frame by frame, and at the same time, the vehicle may computethe camera's ego motion (rotation and translation). At each frame orimage, a short range model for the desired path is generated by thevehicle in a reference frame that is attached to the camera. The shortrange models may be stitched together to obtain a three dimensionalmodel of the road in some coordinate frame, which may be an arbitrary orpredetermined coordinate frame. The three dimensional model of the roadmay then be fitted by a spline, which may include or connect one or morepolynomials of suitable orders.

To conclude the short range road model at each frame, one or moredetection modules may be used. For example, a bottom-up lane detectionmodule may be used. The bottom-up lane detection module may be usefulwhen lane marks are drawn on the road. This module may look for edges inthe image and assembles them together to form the lane marks. A secondmodule may be used together with the bottom-up lane detection module.The second module is an end-to-end deep neural network, which may betrained to predict the correct short range path from an input image. Inboth modules, the road model may be detected in the image coordinateframe and transformed to a three dimensional space that may be virtuallyattached to the camera.

Although the reconstructed trajectory modeling method may introduce anaccumulation of errors due to the integration of ego motion over a longperiod of time, which may include a noise component, such errors may beinconsequential as the generated model may provide sufficient accuracyfor navigation over a local scale. In addition, it is possible to cancelthe integrated error by using external sources of information, such assatellite images or geodetic measurements. For example, the disclosedsystems and methods may use a GNSS receiver to cancel accumulatederrors. However, the GNSS positioning signals may not be alwaysavailable and accurate. The disclosed systems and methods may enable asteering application that depends weakly on the availability andaccuracy of GNSS positioning. In such systems, the usage of the GNSSsignals may be limited. For example, in some embodiments, the disclosedsystems may use the GNSS signals for database indexing purposes only.

In some embodiments, the range scale (e.g., local scale) that may berelevant for an autonomous vehicle navigation steering application maybe on the order of 50 meters, 100 meters, 200 meters, 300 meters, etc.Such distances may be used, as the geometrical road model is mainly usedfor two purposes: planning the trajectory ahead and localizing thevehicle on the road model. In some embodiments, the planning task mayuse the model over a typical range of 40 meters ahead (or any othersuitable distance ahead, such as 20 meters, 30 meters, 50 meters), whenthe control algorithm steers the vehicle according to a target pointlocated 1.3 seconds ahead (or any other time such as 1.5 seconds, 1.7seconds, 2 seconds, etc.). The localization task uses the road modelover a typical range of 60 meters behind the car (or any other suitabledistances, such as 50 meters, 100 meters, 150 meters, etc.), accordingto a method called “tail alignment” described in more detail in anothersection. The disclosed systems and methods may generate a geometricalmodel that has sufficient accuracy over particular range, such as 100meters, such that a planned trajectory will not deviate by more than,for example, 30 cm from the lane center.

As explained above, a three dimensional road model may be constructedfrom detecting short range sections and stitching them together. Thestitching may be enabled by computing a six degree ego motion model,using the videos and/or images captured by the camera, data from theinertial sensors that reflect the motions of the vehicle, and the hostvehicle velocity signal. The accumulated error may be small enough oversome local range scale, such as of the order of 100 meters. All this maybe completed in a single drive over a particular road segment.

In some embodiments, multiple drives may be used to average the resultedmodel, and to increase its accuracy further. The same car may travel thesame route multiple times, or multiple cars may send their collectedmodel data to a central server. In any case, a matching procedure may beperformed to identify overlapping models and to enable averaging inorder to generate target trajectories. The constructed model (e.g.,including the target trajectories) may be used for steering once aconvergence criterion is met. Subsequent drives may be used for furthermodel improvements and in order to accommodate infrastructure changes.

Sharing of driving experience (such as sensed data) between multiplecars becomes feasible if they are connected to a central server. Eachvehicle client may store a partial copy of a universal road model, whichmay be relevant for its current position. A bidirectional updateprocedure between the vehicles and the server may be performed by thevehicles and the server. The small footprint concept discussed aboveenables the disclosed systems and methods to perform the bidirectionalupdates using a very small bandwidth.

Information relating to potential landmarks may also be determined andforwarded to a central server. For example, the disclosed systems andmethods may determine one or more physical properties of a potentiallandmark based on one or more images that include the landmark. Thephysical properties may include a physical size (e.g., height, width) ofthe landmark, a distance from a vehicle to a landmark, a distancebetween the landmark to a previous landmark, the lateral position of thelandmark (e.g., the position of the landmark relative to the lane oftravel), the GPS coordinates of the landmark, a type of landmark,identification of text on the landmark, etc. For example, a vehicle mayanalyze one or more images captured by a camera to detect a potentiallandmark, such as a speed limit sign.

The vehicle may determine a distance from the vehicle to the landmarkbased on the analysis of the one or more images. In some embodiments,the distance may be determined based on analysis of images of thelandmark using a suitable image analysis method, such as a scalingmethod and/or an optical flow method. In some embodiments, the disclosedsystems and methods may be configured to determine a type orclassification of a potential landmark. In case the vehicle determinesthat a certain potential landmark corresponds to a predetermined type orclassification stored in a sparse map, it may be sufficient for thevehicle to communicate to the server an indication of the type orclassification of the landmark, along with its location. The server maystore such indications. At a later time, other vehicles may capture animage of the landmark, process the image (e.g., using a classifier), andcompare the result from processing the image to the indication stored inthe server with regard to the type of landmark. There may be varioustypes of landmarks, and different types of landmarks may be associatedwith different types of data to be uploaded to and stored in the server,different processing onboard the vehicle may detects the landmark andcommunicate information about the landmark to the server, and the systemonboard the vehicle may receive the landmark data from the server anduse the landmark data for identifying a landmark in autonomousnavigation.

In some embodiments, multiple autonomous vehicles travelling on a roadsegment may communicate with a server. The vehicles (or clients) maygenerate a curve describing its drive (e.g., through ego motionintegration) in an arbitrary coordinate frame. The vehicles may detectlandmarks and locate them in the same frame. The vehicles may upload thecurve and the landmarks to the server. The server may collect data fromvehicles over multiple drives, and generate a unified road model. Forexample, as discussed below with respect to FIG. 19, the server maygenerate a sparse map having the unified road model using the uploadedcurves and landmarks.

The server may also distribute the model to clients (e.g., vehicles).For example, as discussed below with respect to FIG. 24, the server maydistribute the sparse map to one or more vehicles. The server maycontinuously or periodically update the model when receiving new datafrom the vehicles. For example, the server may process the new data toevaluate whether the data includes information that should trigger anupdated, or creation of new data on the server. The server maydistribute the updated model or the updates to the vehicles forproviding autonomous vehicle navigation.

The server may use one or more criteria for determining whether new datareceived from the vehicles should trigger an update to the model ortrigger creation of new data. For example, when the new data indicatesthat a previously recognized landmark at a specific location no longerexists, or is replaced by another landmark, the server may determinethat the new data should trigger an update to the model. As anotherexample, when the new data indicates that a road segment has beenclosed, and when this has been corroborated by data received from othervehicles, the server may determine that the new data should trigger anupdate to the model.

The server may distribute the updated model (or the updated portion ofthe model) to one or more vehicles that are traveling on the roadsegment, with which the updates to the model are associated. The servermay also distribute the updated model to vehicles that are about totravel on the road segment, or vehicles whose planned trip includes theroad segment, with which the updates to the model are associated. Forexample, while an autonomous vehicle is traveling along another roadsegment before reaching the road segment with which an update isassociated, the server may distribute the updates or updated model tothe autonomous vehicle before the vehicle reaches the road segment.

In some embodiments, the remote server may collect trajectories andlandmarks from multiple clients (e.g., vehicles that travel along acommon road segment). The server may match curves using landmarks andcreate an average road model based on the trajectories collected fromthe multiple vehicles. The server may also compute a graph of roads andthe most probable path at each node or conjunction of the road segment.For example, as discussed with respect to FIG. 29 below, the remoteserver may align the trajectories to generate a crowdsourced sparse mapfrom the collected trajectories.

The server may average landmark properties received from multiplevehicles that travelled along the common road segment, such as thedistances between one landmark to another (e.g., a previous one alongthe road segment) as measured by multiple vehicles, to determine anarc-length parameter and support localization along the path and speedcalibration for each client vehicle. The server may average the physicaldimensions of a landmark measured by multiple vehicles travelled alongthe common road segment and recognized the same landmark. The averagedphysical dimensions may be used to support distance estimation, such asthe distance from the vehicle to the landmark. The server may averagelateral positions of a landmark (e.g., position from the lane in whichvehicles are travelling in to the landmark) measured by multiplevehicles travelled along the common road segment and recognized the samelandmark. The averaged lateral portion may be used to support laneassignment. The server may average the GPS coordinates of the landmarkmeasured by multiple vehicles travelled along the same road segment andrecognized the same landmark. The averaged GPS coordinates of thelandmark may be used to support global localization or positioning ofthe landmark in the road model.

In some embodiments, the server may identify model changes, such asconstructions, detours, new signs, removal of signs, etc., based on datareceived from the vehicles. The server may continuously or periodicallyor instantaneously update the model upon receiving new data from thevehicles. The server may distribute updates to the model or the updatedmodel to vehicles for providing autonomous navigation. For example, asdiscussed further below, the server may use crowdsourced data to filterout “ghost” landmarks detected by vehicles.

In some embodiments, the server may analyze driver interventions duringthe autonomous driving. The server may analyze data received from thevehicle at the time and location where intervention occurs, and/or datareceived prior to the time the intervention occurred. The server mayidentify certain portions of the data that caused or are closely relatedto the intervention, for example, data indicating a temporary laneclosure setup, data indicating a pedestrian in the road. The server mayupdate the model based on the identified data. For example, the servermay modify one or more trajectories stored in the model.

FIG. 12 is a schematic illustration of a system that uses crowdsourcedto generate a sparse map (as well as distribute and navigate using acrowdsourced sparse map). FIG. 12 shows a road segment 1200 thatincludes one or more lanes. A plurality of vehicles 1205, 1210, 1215,1220, and 1225 may travel on road segment 1200 at the same time or atdifferent times (although shown as appearing on road segment 1200 at thesame time in FIG. 12). At least one of vehicles 1205, 1210, 1215, 1220,and 1225 may be an autonomous vehicle. For simplicity of the presentexample, all of the vehicles 1205, 1210, 1215, 1220, and 1225 arepresumed to be autonomous vehicles.

Each vehicle may be similar to vehicles disclosed in other embodiments(e.g., vehicle 200), and may include components or devices included inor associated with vehicles disclosed in other embodiments. Each vehiclemay be equipped with an image capture device or camera (e.g., imagecapture device 122 or camera 122). Each vehicle may communicate with aremote server 1230 via one or more networks (e.g., over a cellularnetwork and/or the Internet, etc.) through wireless communication paths1235, as indicated by the dashed lines. Each vehicle may transmit datato server 1230 and receive data from server 1230. For example, server1230 may collect data from multiple vehicles travelling on the roadsegment 1200 at different times, and may process the collected data togenerate an autonomous vehicle road navigation model, or an update tothe model. Server 1230 may transmit the autonomous vehicle roadnavigation model or the update to the model to the vehicles thattransmitted data to server 1230. Server 1230 may transmit the autonomousvehicle road navigation model or the update to the model to othervehicles that travel on road segment 1200 at later times.

As vehicles 1205, 1210, 1215, 1220, and 1225 travel on road segment1200, navigation information collected (e.g., detected, sensed, ormeasured) by vehicles 1205, 1210, 1215, 1220, and 1225 may betransmitted to server 1230. In some embodiments, the navigationinformation may be associated with the common road segment 1200. Thenavigation information may include a trajectory associated with each ofthe vehicles 1205, 1210, 1215, 1220, and 1225 as each vehicle travelsover road segment 1200. In some embodiments, the trajectory may bereconstructed based on data sensed by various sensors and devicesprovided on vehicle 1205. For example, the trajectory may bereconstructed based on at least one of accelerometer data, speed data,landmarks data, road geometry or profile data, vehicle positioning data,and ego motion data. In some embodiments, the trajectory may bereconstructed based on data from inertial sensors, such asaccelerometer, and the velocity of vehicle 1205 sensed by a speedsensor. In addition, in some embodiments, the trajectory may bedetermined (e.g., by a processor onboard each of vehicles 1205, 1210,1215, 1220, and 1225) based on sensed ego motion of the camera, whichmay indicate three dimensional translation and/or three dimensionalrotations (or rotational motions). The ego motion of the camera (andhence the vehicle body) may be determined from analysis of one or moreimages captured by the camera.

In some embodiments, the trajectory of vehicle 1205 may be determined bya processor provided aboard vehicle 1205 and transmitted to server 1230.In other embodiments, server 1230 may receive data sensed by the varioussensors and devices provided in vehicle 1205, and determine thetrajectory based on the data received from vehicle 1205.

In some embodiments, the navigation information transmitted fromvehicles 1205, 1210, 1215, 1220, and 1225 to server 1230 may includedata regarding the road surface, the road geometry, or the road profile.The geometry of road segment 1200 may include lane structure and/orlandmarks. The lane structure may include the total number of lanes ofroad segment 1200, the type of lanes (e.g., one-way lane, two-way lane,driving lane, passing lane, etc.), markings on lanes, width of lanes,etc. In some embodiments, the navigation information may include a laneassignment, e.g., which lane of a plurality of lanes a vehicle istraveling in. For example, the lane assignment may be associated with anumerical value “3” indicating that the vehicle is traveling on thethird lane from the left or right. As another example, the laneassignment may be associated with a text value “center lane” indicatingthe vehicle is traveling on the center lane.

Server 1230 may store the navigation information on a non-transitorycomputer-readable medium, such as a hard drive, a compact disc, a tape,a memory, etc. Server 1230 may generate (e.g., through a processorincluded in server 1230) at least a portion of an autonomous vehicleroad navigation model for the common road segment 1200 based on thenavigation information received from the plurality of vehicles 1205,1210, 1215, 1220, and 1225 and may store the model as a portion of asparse map. Server 1230 may determine a trajectory associated with eachlane based on crowdsourced data (e.g., navigation information) receivedfrom multiple vehicles (e.g., 1205, 1210, 1215, 1220, and 1225) thattravel on a lane of road segment at different times. Server 1230 maygenerate the autonomous vehicle road navigation model or a portion ofthe model (e.g., an updated portion) based on a plurality oftrajectories determined based on the crowd sourced navigation data. Asexplained in greater detail below with respect to FIG. 24, server 1230may transmit the model or the updated portion of the model to one ormore of autonomous vehicles 1205, 1210, 1215, 1220, and 1225 travelingon road segment 1200 or any other autonomous vehicles that travel onroad segment at a later time for updating an existing autonomous vehicleroad navigation model provided in a navigation system of the vehicles.As explained in greater detail below with respect to FIG. 26, theautonomous vehicle road navigation model may be used by the autonomousvehicles in autonomously navigating along the common road segment 1200.

As explained above, the autonomous vehicle road navigation model may beincluded in a sparse map (e.g., sparse map 800 depicted in FIG. 8).Sparse map 800 may include sparse recording of data related to roadgeometry and/or landmarks along a road, which may provide sufficientinformation for guiding autonomous navigation of an autonomous vehicle,yet does not require excessive data storage. In some embodiments, theautonomous vehicle road navigation model may be stored separately fromsparse map 800, and may use map data from sparse map 800 when the modelis executed for navigation. In some embodiments, the autonomous vehicleroad navigation model may use map data included in sparse map 800 fordetermining target trajectories along road segment 1200 for guidingautonomous navigation of autonomous vehicles 1205, 1210, 1215, 1220, and1225 or other vehicles that later travel along road segment 1200. Forexample, when the autonomous vehicle road navigation model is executedby a processor included in a navigation system of vehicle 1205, themodel may cause the processor to compare the trajectories determinedbased on the navigation information received from vehicle 1205 withpredetermined trajectories included in sparse map 800 to validate and/orcorrect the current traveling course of vehicle 1205.

In the autonomous vehicle road navigation model, the geometry of a roadfeature or target trajectory may be encoded by a curve in athree-dimensional space. In one embodiment, the curve may be a threedimensional spline including one or more connecting three dimensionalpolynomials. As one of skill in the art would understand, a spline maybe a numerical function that is piece-wise defined by a series ofpolynomials for fitting data. A spline for fitting the three dimensionalgeometry data of the road may include a linear spline (first order), aquadratic spline (second order), a cubic spline (third order), or anyother splines (other orders), or a combination thereof. The spline mayinclude one or more three dimensional polynomials of different ordersconnecting (e.g., fitting) data points of the three dimensional geometrydata of the road. In some embodiments, the autonomous vehicle roadnavigation model may include a three dimensional spline corresponding toa target trajectory along a common road segment (e.g., road segment1200) or a lane of the road segment 1200.

As explained above, the autonomous vehicle road navigation modelincluded in the sparse map may include other information, such asidentification of at least one landmark along road segment 1200. Thelandmark may be visible within a field of view of a camera (e.g., camera122) installed on each of vehicles 1205, 1210, 1215, 1220, and 1225. Insome embodiments, camera 122 may capture an image of a landmark. Aprocessor (e.g., processor 180, 190, or processing unit 110) provided onvehicle 1205 may process the image of the landmark to extractidentification information for the landmark. The landmark identificationinformation, rather than an actual image of the landmark, may be storedin sparse map 800. The landmark identification information may requiremuch less storage space than an actual image. Other sensors or systems(e.g., GPS system) may also provide certain identification informationof the landmark (e.g., position of landmark). The landmark may includeat least one of a traffic sign, an arrow marking, a lane marking, adashed lane marking, a traffic light, a stop line, a directional sign(e.g., a highway exit sign with an arrow indicating a direction, ahighway sign with arrows pointing to different directions or places), alandmark beacon, or a lamppost. A landmark beacon refers to a device(e.g., an RFID device) installed along a road segment that transmits orreflects a signal to a receiver installed on a vehicle, such that whenthe vehicle passes by the device, the beacon received by the vehicle andthe location of the device (e.g., determined from GPS location of thedevice) may be used as a landmark to be included in the autonomousvehicle road navigation model and/or the sparse map 800.

The identification of at least one landmark may include a position ofthe at least one landmark. The position of the landmark may bedetermined based on position measurements performed using sensor systems(e.g., Global Positioning Systems, inertial based positioning systems,landmark beacon, etc.) associated with the plurality of vehicles 1205,1210, 1215, 1220, and 1225. In some embodiments, the position of thelandmark may be determined by averaging the position measurementsdetected, collected, or received by sensor systems on different vehicles1205, 1210, 1215, 1220, and 1225 through multiple drives. For example,vehicles 1205, 1210, 1215, 1220, and 1225 may transmit positionmeasurements data to server 1230, which may average the positionmeasurements and use the averaged position measurement as the positionof the landmark. The position of the landmark may be continuouslyrefined by measurements received from vehicles in subsequent drives.

The identification of the landmark may include a size of the landmark.The processor provided on a vehicle (e.g., 1205) may estimate thephysical size of the landmark based on the analysis of the images.Server 1230 may receive multiple estimates of the physical size of thesame landmark from different vehicles over different drives. Server 1230may average the different estimates to arrive at a physical size for thelandmark, and store that landmark size in the road model. The physicalsize estimate may be used to further determine or estimate a distancefrom the vehicle to the landmark. The distance to the landmark may beestimated based on the current speed of the vehicle and a scale ofexpansion based on the position of the landmark appearing in the imagesrelative to the focus of expansion of the camera. For example, thedistance to landmark may be estimated by Z=V*dt*R/D, where V is thespeed of vehicle, R is the distance in the image from the landmark attime t1 to the focus of expansion, and D is the change in distance forthe landmark in the image from t1 to t2. dt represents the (t2−t1). Forexample, the distance to landmark may be estimated by Z=V*dt*R/D, whereV is the speed of vehicle, R is the distance in the image between thelandmark and the focus of expansion, dt is a time interval, and D is theimage displacement of the landmark along the epipolar line. Otherequations equivalent to the above equation, such as Z=V*ω/Δω, may beused for estimating the distance to the landmark. Here, V is the vehiclespeed, ω is an image length (like the object width), and Δω is thechange of that image length in a unit of time.

When the physical size of the landmark is known, the distance to thelandmark may also be determined based on the following equation:Z=f*W/ω, where f is the focal length, W is the size of the landmark(e.g., height or width), w is the number of pixels when the landmarkleaves the image. From the above equation, a change in distance Z may becalculated using ΔZ=f*W*Δω/ω²+f*ΔW/ω, where ΔW decays to zero byaveraging, and where Δω is the number of pixels representing a boundingbox accuracy in the image. A value estimating the physical size of thelandmark may be calculated by averaging multiple observations at theserver side. The resulting error in distance estimation may be verysmall. There are two sources of error that may occur when using theformula above, namely ΔW and Δω. Their contribution to the distanceerror is given by ΔZ=f*W*Δω/ω²+f*ΔW/ω. However, ΔW decays to zero byaveraging; hence ΔZ is determined by Δω (e.g., the inaccuracy of thebounding box in the image).

For landmarks of unknown dimensions, the distance to the landmark may beestimated by tracking feature points on the landmark between successiveframes. For example, certain features appearing on a speed limit signmay be tracked between two or more image frames. Based on these trackedfeatures, a distance distribution per feature point may be generated.The distance estimate may be extracted from the distance distribution.For example, the most frequent distance appearing in the distancedistribution may be used as the distance estimate. As another example,the average of the distance distribution may be used as the distanceestimate.

FIG. 13 illustrates an example autonomous vehicle road navigation modelrepresented by a plurality of three dimensional splines 1301, 1302, and1303. The curves 1301, 1302, and 1303 shown in FIG. 13 are forillustration purpose only. Each spline may include one or more threedimensional polynomials connecting a plurality of data points 1310. Eachpolynomial may be a first order polynomial, a second order polynomial, athird order polynomial, or a combination of any suitable polynomialshaving different orders. Each data point 1310 may be associated with thenavigation information received from vehicles 1205, 1210, 1215, 1220,and 1225. In some embodiments, each data point 1310 may be associatedwith data related to landmarks (e.g., size, location, and identificationinformation of landmarks) and/or road signature profiles (e.g., roadgeometry, road roughness profile, road curvature profile, road widthprofile). In some embodiments, some data points 1310 may be associatedwith data related to landmarks, and others may be associated with datarelated to road signature profiles.

FIG. 14 illustrates raw location data 1410 (e.g., GPS data) receivedfrom five separate drives. One drive may be separate from another driveif it was traversed by separate vehicles at the same time, by the samevehicle at separate times, or by separate vehicles at separate times. Toaccount for errors in the location data 1410 and for differing locationsof vehicles within the same lane (e.g., one vehicle may drive closer tothe left of a lane than another), server 1230 may generate a mapskeleton 1420 using one or more statistical techniques to determinewhether variations in the raw location data 1410 represent actualdivergences or statistical errors. Each path within skeleton 1420 may belinked back to the raw data 1410 that formed the path. For example, thepath between A and B within skeleton 1420 is linked to raw data 1410from drives 2, 3, 4, and 5 but not from drive 1. Skeleton 1420 may notbe detailed enough to be used to navigate a vehicle (e.g., because itcombines drives from multiple lanes on the same road unlike the splinesdescribed above) but may provide useful topological information and maybe used to define intersections.

FIG. 15 illustrates an example by which additional detail may begenerated for a sparse map within a segment of a map skeleton (e.g.,segment A to B within skeleton 1420). As depicted in FIG. 15, the data(e.g. ego-motion data, road markings data, and the like) may be shown asa function of position S (or S₁ or S₂) along the drive. Server 1230 mayidentify landmarks for the sparse map by identifying unique matchesbetween landmarks 1501, 1503, and 1505 of drive 1510 and landmarks 1507and 1509 of drive 1520. Such a matching algorithm may result inidentification of landmarks 1511, 1513, and 1515. One skilled in the artwould recognize, however, that other matching algorithms may be used.For example, probability optimization may be used in lieu of or incombination with unique matching. As described in further detail belowwith respect to FIG. 29, server 1230 may longitudinally align the drivesto align the matched landmarks. For example, server 1230 may select onedrive (e.g., drive 1520) as a reference drive and then shift and/orelastically stretch the other drive(s) (e.g., drive 1510) for alignment.

FIG. 16 shows an example of aligned landmark data for use in a sparsemap. In the example of FIG. 16, landmark 1610 comprises a road sign. Theexample of FIG. 16 further depicts data from a plurality of drives 1601,1603, 1605, 1607, 1609, 1611, and 1613. In the example of FIG. 16, thedata from drive 1613 consists of a “ghost” landmark, and the server 1230may identify it as such because none of drives 1601, 1603, 1605, 1607,1609, and 1611 include an identification of a landmark in the vicinityof the identified landmark in drive 1613. Accordingly, server 1230 mayaccept potential landmarks when a ratio of images in which the landmarkdoes appear to images in which the landmark does not appear exceeds athreshold and/or may reject potential landmarks when a ratio of imagesin which the landmark does not appear to images in which the landmarkdoes appear exceeds a threshold.

FIG. 17 depicts a system 1700 for generating drive data, which may beused to crowdsource a sparse map. As depicted in FIG. 17, system 1700may include a camera 1701 and a locating device 1703 (e.g., a GPSlocator). Camera 1701 and locating device 1703 may be mounted on avehicle (e.g., one of vehicles 1205, 1210, 1215, 1220, and 1225). Camera1701 may produce a plurality of data of multiple types, e.g., ego motiondata, traffic sign data, road data, or the like. The camera data andlocation data may be segmented into drive segments 1705. For example,drive segments 1705 may each have camera data and location data fromless than 1 km of driving.

In some embodiments, system 1700 may remove redundancies in drivesegments 1705. For example, if a landmark appears in multiple imagesfrom camera 1701, system 1700 may strip the redundant data such that thedrive segments 1705 only contain one copy of the location of and anymetadata relating to the landmark. By way of further example, if a lanemarking appears in multiple images from camera 1701, system 1700 maystrip the redundant data such that the drive segments 1705 only containone copy of the location of and any metadata relating to the lanemarking.

System 1700 also includes a server (e.g., server 1230). Server 1230 mayreceive drive segments 1705 from the vehicle and recombine the drivesegments 1705 into a single drive 1707. Such an arrangement may allowfor reduce bandwidth requirements when transferring data between thevehicle and the server while also allowing for the server to store datarelating to an entire drive.

FIG. 18 depicts system 1700 of FIG. 17 further configured forcrowdsourcing a sparse map. As in FIG. 17, system 1700 includes vehicle1810, which captures drive data using, for example, a camera (whichproduces, e.g., ego motion data, traffic sign data, road data, or thelike) and a locating device (e.g., a GPS locator). As in FIG. 17,vehicle 1810 segments the collected data into drive segments (depictedas “DS1 1,” “DS2 1,” “DSN 1” in FIG. 18). Server 1230 then receives thedrive segments and reconstructs a drive (depicted as “Drive 1” in FIG.18) from the received segments.

As further depicted in FIG. 18, system 1700 also receives data fromadditional vehicles. For example, vehicle 1820 also captures drive datausing, for example, a camera (which produces, e.g., ego motion data,traffic sign data, road data, or the like) and a locating device (e.g.,a GPS locator). Similar to vehicle 1810, vehicle 1820 segments thecollected data into drive segments (depicted as “DS1 2,” “DS2 2,” “DSN2” in FIG. 18). Server 1230 then receives the drive segments andreconstructs a drive (depicted as “Drive 2” in FIG. 18) from thereceived segments. Any number of additional vehicles may be used. Forexample, FIG. 18 also includes “CAR N” that captures drive data,segments it into drive segments (depicted as “DS1 N,” “DS2 N,” “DSN N”in FIG. 18), and sends it to server 1230 for reconstruction into a drive(depicted as “Drive N” in FIG. 18).

As depicted in FIG. 18, server 1230 may construct a sparse map (depictedas “MAP”) using the reconstructed drives (e.g., “Drive 1,” “Drive 2,”and “Drive N”) collected from a plurality of vehicles (e.g., “CAR 1”(also labeled vehicle 1810), “CAR 2” (also labeled vehicle 1820), and“CAR N”).

FIG. 19 is a flowchart showing an example process 1900 for generating asparse map for autonomous vehicle navigation along a road segment.Process 1900 may be performed by one or more processing devices includedin server 1230.

Process 1900 may include receiving a plurality of images acquired as oneor more vehicles traverse the road segment (step 1905). Server 1230 mayreceive images from cameras included within one or more of vehicles1205, 1210, 1215, 1220, and 1225. For example, camera 122 may captureone or more images of the environment surrounding vehicle 1205 asvehicle 1205 travels along road segment 1200. In some embodiments,server 1230 may also receive stripped down image data that has hadredundancies removed by a processor on vehicle 1205, as discussed abovewith respect to FIG. 17.

Process 1900 may further include identifying, based on the plurality ofimages, at least one line representation of a road surface featureextending along the road segment (step 1910). Each line representationmay represent a path along the road segment substantially correspondingwith the road surface feature. For example, server 1230 may analyze theenvironmental images received from camera 122 to identify a road edge ora lane marking and determine a trajectory of travel along road segment1200 associated with the road edge or lane marking. In some embodiments,the trajectory (or line representation) may include a spline, apolynomial representation, or a curve. Server 1230 may determine thetrajectory of travel of vehicle 1205 based on camera ego motions (e.g.,three dimensional translation and/or three dimensional rotationalmotions) received at step 1905.

Process 1900 may also include identifying, based on the plurality ofimages, a plurality of landmarks associated with the road segment (step1910). For example, server 1230 may analyze the environmental imagesreceived from camera 122 to identify one or more landmarks, such as roadsign along road segment 1200. Server 1230 may identify the landmarksusing analysis of the plurality of images acquired as one or morevehicles traverse the road segment. To enable crowdsourcing, theanalysis may include rules regarding accepting and rejecting possiblelandmarks associated with the road segment. For example, the analysismay include accepting potential landmarks when a ratio of images inwhich the landmark does appear to images in which the landmark does notappear exceeds a threshold and/or rejecting potential landmarks when aratio of images in which the landmark does not appear to images in whichthe landmark does appear exceeds a threshold.

Process 1900 may include other operations or steps performed by server123. For example, the navigation information may include a targettrajectory for vehicles to travel along a road segment, and process 1900may include clustering, by server 1230, vehicle trajectories related tomultiple vehicles travelling on the road segment and determining thetarget trajectory based on the clustered vehicle trajectories, asdiscussed in further detail below. Clustering vehicle trajectories mayinclude clustering, by server 1230, the multiple trajectories related tothe vehicles travelling on the road segment into a plurality of clustersbased on at least one of the absolute heading of vehicles or laneassignment of the vehicles. Generating the target trajectory may includeaveraging, by server 1230, the clustered trajectories.

By way of further example, process 1900 may include aligning datareceived in step 1905, as discussed in further detail below with respectto FIG. 29. Other processes or steps performed by server 1230, asdescribed above, may also be included in process 1900.

The disclosed systems and methods may include other features. Forexample, the disclosed systems may use local coordinates, rather thanglobal coordinates. For autonomous driving, some systems may presentdata in world coordinates. For example, longitude and latitudecoordinates on the earth surface may be used. In order to use the mapfor steering, the host vehicle may determine its position andorientation relative to the map. It seems natural to use a GPS device onboard, in order to position the vehicle on the map and in order to findthe rotation transformation between the body reference frame and theworld reference frame (e.g., North, East and Down). Once the bodyreference frame is aligned with the map reference frame, then thedesired route may be expressed in the body reference frame and thesteering commands may be computed or generated.

However, one possible issue with this strategy is that current GPStechnology does not usually provide the body location and position withsufficient accuracy and availability. To overcome this problem,landmarks whose world coordinates are known may be used to constructvery detailed maps (called High Definition or HD maps), that containlandmarks of different kinds. Accordingly, a vehicle equipped with asensor may detect and locate the landmarks in its own reference frame.Once the relative position between the vehicle and the landmarks isfound, the landmarks' world coordinates may be determined from the HDmap, and the vehicle may use them to compute its own location andposition.

This method may nevertheless use the global world coordinate system as amediator that establishes the alignment between the map and the bodyreference frames. Namely, the landmarks may be used in order tocompensate for the limitations of the GPS device onboard the vehicles.The landmarks, together with an HD map, may enable to compute theprecise vehicle position in global coordinates, and hence the map-bodyalignment problem is solved.

In the disclosed systems and methods, instead of using one global map ofthe world, many map pieces or local maps may be used for autonomousnavigation. Each piece of a map or each local map may define its owncoordinate frame. These coordinate frames may be arbitrary. Thevehicle's coordinates in the local maps may not need to indicate wherethe vehicle is located on the surface of earth. Moreover, the local mapsmay not be required to be accurate over large scales, meaning there maybe no rigid transformation that can embed a local map in the globalworld coordinate system.

There are two main processes associated with this representation of theworld, one relates to the generation of the maps and the other relatesto using them. With respect to map generation, this type ofrepresentation may be created and maintained by crowdsourcing. There maybe no need to apply sophisticated survey equipment, because the use ofHD maps is limited, and hence crowd sourcing becomes feasible. Withrespect to usage, an efficient method to align the local map with thebody reference frame without going through a standard world coordinatesystem may be employed. Hence there may be no need, at least in mostscenarios and circumstances, to have a precise estimation of the vehiclelocation and position in global coordinates. Further, the memoryfootprint of the local maps may be kept very small.

The principle underlying the maps generation is the integration of egomotion. The vehicles may sense the motion of the camera in space (3Dtranslation and 3D rotation). The vehicles or the server may reconstructthe trajectory of the vehicle by integration of ego motion over time,and this integrated path may be used as a model for the road geometry.This process may be combined with sensing of close range lane marks, andthen the reconstructed route may reflect the path that a vehicle shouldfollow, and not the particular path that the vehicle did follow. Inother words, the reconstructed route or trajectory may be modified basedon the sensed data relating to close range lane marks, and the modifiedreconstructed trajectory may be used as a recommended trajectory ortarget trajectory, which may be saved in the road model or sparse mapfor use by other vehicles navigating the same road segment.

In some embodiments, the map coordinate system may be arbitrary. Acamera reference frame may be selected at an arbitrary time, and used asthe map origin. The integrated trajectory of the camera may be expressedin the coordinate system of that particular chosen frame. The value ofthe route coordinates in the map may not directly represent a locationon earth.

The integrated path may accumulate errors. This may be due to the factthat the sensing of the ego motion may not be absolutely accurate. Theresult of the accumulated error is that the local map may diverge, andthe local map may not be regarded as a local copy of the global map. Thelarger the size of the local map piece, the larger the deviation fromthe “true” geometry on earth.

The arbitrariness and the divergence of the local maps may not be aconsequence of the integration method, which may be applied in order toconstruct the maps in a crowdsourcing manner (e.g., by vehiclestraveling along the roads). However, vehicles may successfully use thelocal maps for steering.

The map may diverge over long distances. Since the map is used to plan atrajectory in the immediate vicinity of the vehicle, the effect of thedivergence may be acceptable. At any time instance, the system (e.g.,server 1230 or vehicle 1205) may repeat the alignment procedure, and usethe map to predict the road location (in the camera coordinate frame)some 1.3 seconds ahead (or any other seconds, such as 1.5 seconds, 1.0second, 1.8 seconds, etc.). As long as the accumulated error over thatdistance is small enough, then the steering command provided forautonomous driving may be used.

In some embodiments, a local map may focus on a local area, and may notcover a too large area. This means that a vehicle that is using a localmap for steering in autonomous driving, may arrive at some point to theend of the map and may have to switch to another local piece or sectionof a map. The switching may be enabled by the local maps overlappingeach other. Once the vehicle enters the area that is common to bothmaps, the system (e.g., server 1230 or vehicle 1205) may continue togenerate steering commands based on a first local map (the map that isbeing used), but at the same time the system may localize the vehicle onthe other map (or second local map) that overlaps with the first localmap. In other words, the system may simultaneously align the presentcoordinate frame of the camera both with the coordinate frame of thefirst map and with the coordinate frame of the second map. When the newalignment is established, the system may switch to the other map andplan the vehicle trajectory there.

The disclosed systems may include additional features, one of which isrelated to the way the system aligns the coordinate frames of thevehicle and the map. As explained above that landmarks may be used foralignment, assuming the vehicle may measure its relative position tothem. This is useful in autonomous driving, but sometimes it may resultin a demand for a large number of landmarks and hence a large memoryfootprint. The disclosed systems may therefore use an alignmentprocedure that addresses this problem. In the alignment procedure, thesystem may compute a 1D estimator for the location of the vehicle alongthe road, using sparse landmarks and integration of ego speed. Thesystem may use the shape of the trajectory itself to compute therotation part of the alignment, using a tail alignment method discussedin details below in other sections. Accordingly, the vehicle mayreconstruct its own trajectory while driving the “tail” and computes arotation around its assumed position along the road, in order to alignthe tail with the map. Such an alignment procedure is distinct from thealignment of the crowdsourced data discussed below with respect to FIG.29.

In the disclosed systems and methods, a GPS device may still be used.Global coordinates may be used for indexing the database that stores thetrajectories and/or landmarks. The relevant piece of local map and therelevant landmarks in the vicinity of the vehicles may be stored inmemory and retrieved from the memory using global GPS coordinates.However, in some embodiments, the global coordinates may not be used forpath planning, and may not be accurate. In one example, the usage ofglobal coordinates may be limited for indexing of the information.

In situations where “tail alignment” cannot function well, the systemmay compute the vehicle's position using a larger number of landmarks.This may be a rare case, and hence the impact on the memory footprintmay be moderate. Road intersections are examples of such situations.

The disclosed systems and methods may use semantic landmarks (e.g.,traffic signs), since they can be reliably detected from the scene andmatched with the landmarks stored in the road model or sparse map. Insome cases, the disclosed systems may use non-semantic landmarks (e.g.,general purpose signs) as well, and in such cases the non-semanticlandmarks may be attached to an appearance signature, as discussedabove. The system may use a learning method for the generation ofsignatures that follows the “same or not-same” recognition paradigm.

For example, given many drives with GPS coordinates along them, thedisclosed systems may produce the underlying road structure junctionsand road segments, as discussed above with respect to FIG. 14. The roadsare assumed to be far enough from each other to be able to differentiatethem using the GPS. In some embodiments, only a coarse grained map maybe needed. To generate the underlying road structure graph, the spacemay be divided into a lattice of a given resolution (e.g., 50 m by 50m). Every drive may be seen as an ordered list of lattice sites. Thesystem may color every lattice site belonging to a drive to produce animage of the merged drives. The colored lattice points may berepresented as nodes on the merged drives. The drives passing from onenode to another may be represented as links. The system may fill smallholes in the image, to avoid differentiating lanes and correct for GPSerrors. The system may use a suitable thinning algorithm (e.g., analgorithm named “Zhang-Suen” thinning algorithm) to obtain the skeletonof the image. This skeleton may represent the underlying road structure,and junctions may be found using a mask (e.g., a point connected to atleast three others). After the junctions are found, the segments may bethe skeleton parts that connect them. To match the drives back to theskeleton, the system may use a Hidden Markov Model. Every GPS point maybe associated with a lattice site with a probability inverse to itsdistance from that site. Use a suitable algorithm (e.g., an algorithmnamed the “Viterbi” algorithm) to match GPS points to lattice sites,while not allowing consecutive GPS points to match to non-neighboringlattice sites.

A plurality of methods may be used for mapping the drives back to themap. For example, a first solution may include keeping track during thethinning process. A second solution may use proximity matching. A thirdsolution may use hidden Markov model. The hidden Markov model assumes anunderlying hidden state for every observation, and assigns probabilitiesfor a given observation given the state, and for a state given theprevious state. A Viterbi algorithm may be used to find the mostprobable states given a list of observations.

The disclosed systems and methods may include additional features. Forexample, the disclosed systems and methods may detect highwayentrances/exits. Multiple drives in the same area may be merged usingGPS data to the same coordinate system. The system may use visualfeature points for mapping and localization.

In some embodiments, generic visual features may be used as landmarksfor the purpose of registering the position and orientation of a movingvehicle, in one drive (localization phase), relative to a map generatedby vehicles traversing the same stretch of road in previous drives(mapping phase). These vehicles may be equipped with calibrated camerasimaging the vehicle surroundings and GPS receivers. The vehicles maycommunicate with a central server (e.g., server 1230) that maintains anup-to-date map including these visual landmarks connected to othersignificant geometric and semantic information (e.g. lane structure,type and position of road signs, type and position of road marks, shapeof nearby drivable ground area delineated by the position of physicalobstacles, shape of previously driven vehicle path when controlled byhuman driver, etc.). The total amount of data that may be communicatedbetween the central server and vehicles per length of road is small,both in a mapping and localization phases.

In a mapping phase, disclosed systems (e.g., autonomous vehicles and/orone or more servers) may detect feature points (FPs). Feature points maycomprise one or more points that are used to track an associated objectsuch as a landmark. For example, the eight points comprising the cornersof a stop sign may be feature points. Disclosed systems may furthercompute descriptors associated with the FPs (e.g., using the featuresfrom the accelerated segment test (FAST) detector, the binary robustinvariant scalable keypoints (BRISK) detector, the binary robustindependent elementary features (BRIEF) detector, and/or the orientedFAST and rotated BRIEF (ORB) detector or using a detector/descriptorpair that was trained using a training library). The system may trackFPs between frames in which they appear using their motion in the imageplane and by matching the associated descriptors using, for example,Euclidean or Hamming distance in descriptor space. The system may usetracked FPs to estimate camera motion and world positions of objects onwhich FPs were detected and tracked. For example, tracked FPs may beused to estimate the motion of the vehicle and/or the position of alandmark on which the FPs were initially detected.

The system may further classify FPs as ones that will likely be detectedin future drives or not (e.g., FPs detected on momentarily movingobjects, parked cars, and shadow texture will likely not reappear infuture drives). This classification may be referred to as areproducibility classification (RC) and may be a function of theintensities of the light in a region of a pyramid surrounding thedetected FP, the motion of the tracked FP in the image plane, and/or theextent of viewpoints in which it was successfully detected and tracked.In some embodiments, the vehicles may send descriptors associated withan FP, estimated 3D position relative to the vehicle of the FP, andmomentary vehicle GPS coordinates at the time of detecting/tracking theFP, to server 1230.

During a mapping phase, when communication bandwidth between mappingvehicles and a central server is limited, the vehicles may send FPs tothe server at a high frequency when the presence of FPs or othersemantic landmarks in the map (such as road signs and lane structure) islimited and insufficient for the purpose of localization. Moreover,although vehicles in the mapping phase may generally send FPs to theserver at a low spatial frequency, the FPs may be agglomerated in theserver. Detection of reoccurring FPs may also be performed by the serverand the server may store the set of reoccurring FPs and/or disregard FPsthat do not reoccur. Visual appearance of landmarks may, at least insome cases, be sensitive to the time of day or the season in which theywere captured. Accordingly, to increase reproducibility probability ofFPs, the receive FPs may be binned by the server into time-of-day bins,season bins, and the like. In some embodiments, the vehicles may alsosend the server other semantic and geometric information associated withthe FPs (e.g., lane shape, structure of road plane, 3D position ofobstacles, free space in mapping clip momentary coordinate system, pathdriven by human driver in a setup drive to a parking location, etc.).

In a localization phase, the server may send a map containing landmarksin the form of FP positions and descriptors to one or more vehicles.Feature points (FPs) may be detected and tracked by the vehicles in nearreal time within a set of current consecutive frames. Tracked FPs may beused to estimate camera motion and/or positions of associated objectssuch as landmarks. Detected FP descriptors may be searched to match alist of FPs included in the map and having GPS coordinates within anestimated finite GPS uncertainty radius from the current GPS reading ofthe vehicle. Matching may be done by searching all pairs of current andmapping FPs that minimize an Euclidean or Hamming distance in descriptorspace. Using the FP matches and their current and map positions, thevehicle may rotate and/or translate between the momentary vehicleposition and the local map coordinate system.

The disclosed systems and methods may include a method for training areproducibility classifier. Training may be performed in one of thefollowing schemes in order of growing labeling cost and resultingclassifier accuracy.

In the first scheme, a database including a large number of clipsrecorded by vehicle cameras with matching momentary vehicle GPS positionmay be collected. This database may include a representative sample ofdrives (with respect to various properties: e.g., time of day, season,weather condition, type of roadway). Feature points (FPs) extracted fromframes of different drives at a similar GPS position and heading may bepotentially matched within a GPS uncertainty radius. Unmatched FPs maybe labeled unreproducible and those matched may be labeled reproducible.A classifier may then be trained to predict the reproducibility label ofan FP given its appearance in the image pyramid, its momentary positionrelative to the vehicle and the extent of viewpoints positions in whichit was successfully tracked.

In the second scheme, FP pairs extracted from the clip databasedescribed in the first scheme may also be labeled by a human responsiblefor annotating FP matches between clips.

In a third scheme, a database augmenting that of the first scheme withprecise vehicle position, vehicle orientation and image pixel depthusing Light Detection And Ranging (LIDAR) measurements may be used toaccurately match world positions in different drives. Feature pointdescriptors may then be computed at the image region corresponding tothese world points at different viewpoints and drive times. Theclassifier may then be trained to predict the average distance indescriptor space a descriptor is located from its matched descriptors.In this case reproducibility may be measured by likely having a lowdescriptor distance.

Consistent with disclosed embodiments, the system may generate anautonomous vehicle road navigation model based on the observedtrajectories of vehicles traversing a common road segment (e.g., whichmay correspond to the trajectory information forwarded to a server by avehicle). The observed trajectories, however, may not correspond toactual trajectories taken by vehicles traversing a road segment. Rather,in certain situations, the trajectories uploaded to the server may bemodified with respect to actual reconstructed trajectories determined bythe vehicles. For example, a vehicle system, while reconstructing atrajectory actually taken, may use sensor information (e.g., analysis ofimages provided by a camera) to determine that its own trajectory maynot be the preferred trajectory for a road segment. For example, thevehicle may determine based on image data from onboard cameras that itis not driving in a center of a lane or that it crossed over a laneboundary for a determined period of time. In such cases, among others, arefinement to the vehicle's reconstructed trajectory (the actual pathtraversed) may be made based on information derived from the sensoroutput. The refined trajectory, not the actual trajectory, may then beuploaded to the server for potential use in building or updating sparsedata map 800.

In some embodiments, then, a processor included in a vehicle (e.g.,vehicle 1205) may determine an actual trajectory of vehicle 1205 basedon the outputs from the one or more sensors. For example, based onanalysis of images output from camera 122, the processor may identifylandmarks along road segment 1200. Landmarks may include traffic signs(e.g., speed limit signs), directional signs (e.g., highway directionalsigns pointing to different routes or places), and general signs (e.g.,a rectangular business sign that is associated with a unique signature,such as a color pattern). The identified landmark may be compared withthe landmark stored in sparse map 800. When a match is found, thelocation of the landmark stored in sparse map 800 may be used as thelocation of the identified landmark. The location of the identifiedlandmark may be used for determining the location of the vehicle 1205along a target trajectory. In some embodiments, the processor may alsodetermine the location of vehicle 1205 based on GPS signals output byGPS unit 1710.

The processor may also determine a target trajectory for transmitting toserver 1230. The target trajectory may be the same as the actualtrajectory determined by the processor based on the sensor outputs. Insome embodiments, though, the target trajectory may be different fromthe actual trajectory determined based on the sensor outputs. Forexample, the target trajectory may include one or more modifications tothe actual trajectory.

In one example, if data from camera 122 includes a barrier, such as atemporary lane shifting barrier 100 meters ahead of vehicle 1250 thatchanges the lanes (e.g., when lanes are temporarily shifted due toconstructions or an accident ahead), the processor may detect thetemporary lane shifting barrier from the image, and select a lanedifferent from a lane corresponding to the target trajectory stored inthe road model or sparse map in compliance to the temporary lane shift.The actual trajectory of vehicle may reflect this change of lanes.However, if the lane shifting is temporary and may be cleared in thenext 10, 15, or 30 minutes, for example, vehicle 1205 may thus modifythe actual trajectory (i.e., the shift of lanes) vehicle 1205 has takento reflect that a target trajectory should be different from the actualtrajectory vehicle 1205 has taken. For example, the system may recognizethat the path traveled differs from a preferred trajectory for the roadsegment. Thus, the system may adjust a reconstructed trajectory prior touploading the trajectory information to the servers.

In other embodiments, the actual reconstructed trajectory informationmay be uploaded, by one or more recommended trajectory refinements(e.g., a size and direction of a translation to be made to at least aportion of the reconstructed trajectory) may also be uploaded. In someembodiments, processor 1715 may transmit a modified actual trajectory toserver 1230. Server 1230 may generate or update a target trajectorybased on the received information and may transmit the target trajectoryto other autonomous vehicles that later travel on the same road segment,as discussed in further detail below with respect to FIG. 24.

As another example, the environmental image may include an object, suchas a pedestrian suddenly appearing in road segment 1200. The processormay detect the pedestrian, and vehicle 1205 may change lanes to avoid acollision with the pedestrian. The actual trajectory vehicle 1205reconstructed based on sensed data may include the change of lanes.However, the pedestrian may soon leave the roadway. So, vehicle 1205 maymodify the actual trajectory (or determine a recommended modification)to reflect that the target trajectory should be different from theactual trajectory taken (as the appearance of the pedestrian is atemporary condition that should not be accounted for in the targettrajectory determination. In some embodiments, the vehicle may transmitto the server data indicating a temporary deviation from thepredetermined trajectory, when the actual trajectory is modified. Thedata may indicate a cause of the deviation, or the server may analyzethe data to determine a cause of the deviation. Knowing the cause of thedeviation may be useful. For example, when the deviation is due to thedriver noticing an accident that has recently occurred and, in responsesteering the wheel to avoid collision, the server may plan a moremoderate adjustment to the model or a specific trajectory associatedwith the road segment based on the cause of deviation. As anotherexample, when the cause of deviation is a pedestrian crossing the road,the server may determine that there is no need to change the trajectoryin the future.

By way of further example, the environmental image may include a lanemarking indicating that vehicle 1205 is driving slightly outside of alane, perhaps under the control of a human driver. The processor maydetect the lane marking from the captured images and may modify theactual trajectory of vehicle 1205 to account for the departure from thelane. For example, a translation may be applied to the reconstructedtrajectory so that it falls within the center of an observed lane.

Distributing Crowdsourced Sparse Maps

The disclosed systems and methods may enable autonomous vehiclenavigation (e.g., steering control) with low footprint models, which maybe collected by the autonomous vehicles themselves without the aid ofexpensive surveying equipment. To support the autonomous navigation(e.g., steering applications), the road model may include a sparse maphaving the geometry of the road, its lane structure, and landmarks thatmay be used to determine the location or position of vehicles along atrajectory included in the model. As discussed above, generation of thesparse map may be performed by a remote server that communicates withvehicles travelling on the road and that receives data from thevehicles. The data may include sensed data, trajectories reconstructedbased on the sensed data, and/or recommended trajectories that mayrepresent modified reconstructed trajectories. As discussed below, theserver may transmit the model back to the vehicles or other vehiclesthat later travel on the road to aid in autonomous navigation.

FIG. 20 illustrates a block diagram of server 1230. Server 1230 mayinclude a communication unit 2005, which may include both hardwarecomponents (e.g., communication control circuits, switches, andantenna), and software components (e.g., communication protocols,computer codes). For example, communication unit 2005 may include atleast one network interface. Server 1230 may communicate with vehicles1205, 1210, 1215, 1220, and 1225 through communication unit 2005. Forexample, server 1230 may receive, through communication unit 2005,navigation information transmitted from vehicles 1205, 1210, 1215, 1220,and 1225. Server 1230 may distribute, through communication unit 2005,the autonomous vehicle road navigation model to one or more autonomousvehicles.

Server 1230 may include at least one non-transitory storage medium 2010,such as a hard drive, a compact disc, a tape, etc. Storage device 1410may be configured to store data, such as navigation information receivedfrom vehicles 1205, 1210, 1215, 1220, and 1225 and/or the autonomousvehicle road navigation model that server 1230 generates based on thenavigation information. Storage device 2010 may be configured to storeany other information, such as a sparse map (e.g., sparse map 800discussed above with respect to FIG. 8).

In addition to or in place of storage device 2010, server 1230 mayinclude a memory 2015. Memory 2015 may be similar to or different frommemory 140 or 150. Memory 2015 may be a non-transitory memory, such as aflash memory, a random access memory, etc. Memory 2015 may be configuredto store data, such as computer codes or instructions executable by aprocessor (e.g., processor 2020), map data (e.g., data of sparse map800), the autonomous vehicle road navigation model, and/or navigationinformation received from vehicles 1205, 1210, 1215, 1220, and 1225.

Server 1230 may include at least one processing device 2020 configuredto execute computer codes or instructions stored in memory 2015 toperform various functions. For example, processing device 2020 mayanalyze the navigation information received from vehicles 1205, 1210,1215, 1220, and 1225, and generate the autonomous vehicle roadnavigation model based on the analysis. Processing device 2020 maycontrol communication unit 1405 to distribute the autonomous vehicleroad navigation model to one or more autonomous vehicles (e.g., one ormore of vehicles 1205, 1210, 1215, 1220, and 1225 or any vehicle thattravels on road segment 1200 at a later time). Processing device 2020may be similar to or different from processor 180, 190, or processingunit 110.

FIG. 21 illustrates a block diagram of memory 2015, which may storecomputer code or instructions for performing one or more operations forgenerating a road navigation model for use in autonomous vehiclenavigation. As shown in FIG. 21, memory 2015 may store one or moremodules for performing the operations for processing vehicle navigationinformation. For example, memory 2015 may include a model generatingmodule 2105 and a model distributing module 2110. Processor 2020 mayexecute the instructions stored in any of modules 2105 and 2110 includedin memory 2015.

Model generating module 2105 may store instructions which, when executedby processor 2020, may generate at least a portion of an autonomousvehicle road navigation model for a common road segment (e.g., roadsegment 1200) based on navigation information received from vehicles1205, 1210, 1215, 1220, and 1225. For example, in generating theautonomous vehicle road navigation model, processor 2020 may clustervehicle trajectories along the common road segment 1200 into differentclusters. Processor 2020 may determine a target trajectory along thecommon road segment 1200 based on the clustered vehicle trajectories foreach of the different clusters. Such an operation may include finding amean or average trajectory of the clustered vehicle trajectories (e.g.,by averaging data representing the clustered vehicle trajectories) ineach cluster. In some embodiments, the target trajectory may beassociated with a single lane of the common road segment 1200.

The autonomous vehicle road navigation model may include a plurality oftarget trajectories each associated with a separate lane of the commonroad segment 1200. In some embodiments, the target trajectory may beassociated with the common road segment 1200 instead of a single lane ofthe road segment 1200. The target trajectory may be represented by athree dimensional spline. In some embodiments, the spline may be definedby less than 10 kilobytes per kilometer, less than 20 kilobytes perkilometer, less than 100 kilobytes per kilometer, less than 1 megabyteper kilometer, or any other suitable storage size per kilometer. Modeldistributing module 2110 may then distribute the generated model to oneor more vehicle, e.g., as discussed below with respect to FIG. 24.

The road model and/or sparse map may store trajectories associated witha road segment. These trajectories may be referred to as targettrajectories, which are provided to autonomous vehicles for autonomousnavigation. The target trajectories may be received from multiplevehicles, or may be generated based on actual trajectories orrecommended trajectories (actual trajectories with some modifications)received from multiple vehicles. The target trajectories included in theroad model or sparse map may be continuously updated (e.g., averaged)with new trajectories received from other vehicles.

Vehicles travelling on a road segment may collect data by varioussensors. The data may include landmarks, road signature profile, vehiclemotion (e.g., accelerometer data, speed data), vehicle position (e.g.,GPS data), and may either reconstruct the actual trajectoriesthemselves, or transmit the data to a server, which will reconstruct theactual trajectories for the vehicles. In some embodiments, the vehiclesmay transmit data relating to a trajectory (e.g., a curve in anarbitrary reference frame), landmarks data, and lane assignment alongtraveling path to server 1230. Various vehicles travelling along thesame road segment at multiple drives may have different trajectories.Server 1230 may identify routes or trajectories associated with eachlane from the trajectories received from vehicles through a clusteringprocess.

FIG. 22 illustrates a process of clustering vehicle trajectoriesassociated with vehicles 1205, 1210, 1215, 1220, and 1225 fordetermining a target trajectory for the common road segment (e.g., roadsegment 1200). The target trajectory or a plurality of targettrajectories determined from the clustering process may be included inthe autonomous vehicle road navigation model or sparse map 800. In someembodiments, vehicles 1205, 1210, 1215, 1220, and 1225 traveling alongroad segment 1200 may transmit a plurality of trajectories 2200 toserver 1230. In some embodiments, server 1230 may generate trajectoriesbased on landmark, road geometry, and vehicle motion informationreceived from vehicles 1205, 1210, 1215, 1220, and 1225. To generate theautonomous vehicle road navigation model, server 1230 may clustervehicle trajectories 1600 into a plurality of clusters 2205, 2210, 2215,2220, 2225, and 2230, as shown in FIG. 22.

Clustering may be performed using various criteria. In some embodiments,all drives in a cluster may be similar with respect to the absoluteheading along the road segment 1200. The absolute heading may beobtained from GPS signals received by vehicles 1205, 1210, 1215, 1220,and 1225. In some embodiments, the absolute heading may be obtainedusing dead reckoning. Dead reckoning, as one of skill in the art wouldunderstand, may be used to determine the current position and henceheading of vehicles 1205, 1210, 1215, 1220, and 1225 by using previouslydetermined position, estimated speed, etc. Trajectories clustered byabsolute heading may be useful for identifying routes along theroadways.

In some embodiments, all the drives in a cluster may be similar withrespect to the lane assignment (e.g., in the same lane before and aftera junction) along the drive on road segment 1200. Trajectories clusteredby lane assignment may be useful for identifying lanes along theroadways. In some embodiments, both criteria (e.g., absolute heading andlane assignment) may be used for clustering.

In each cluster 2205, 2210, 2215, 2220, 2225, and 2230, trajectories maybe averaged to obtain a target trajectory associated with the specificcluster. For example, the trajectories from multiple drives associatedwith the same lane cluster may be averaged. The averaged trajectory maybe a target trajectory associate with a specific lane. To average acluster of trajectories, server 1230 may select a reference frame of anarbitrary trajectory C0. For all other trajectories (C1, . . . , Cn),server 1230 may find a rigid transformation that maps Ci to C0, wherei=1, 2, . . . , n, where n is a positive integer number, correspondingto the total number of trajectories included in the cluster. Server 1230may compute a mean curve or trajectory in the C0 reference frame.

In some embodiments, the landmarks may define an arc length matchingbetween different drives, which may be used for alignment oftrajectories with lanes. In some embodiments, lane marks before andafter a junction may be used for alignment of trajectories with lanes.

To assemble lanes from the trajectories, server 1230 may select areference frame of an arbitrary lane. Server 1230 may map partiallyoverlapping lanes to the selected reference frame. Server 1230 maycontinue mapping until all lanes are in the same reference frame. Lanesthat are next to each other may be aligned as if they were the samelane, and later they may be shifted laterally.

Landmarks recognized along the road segment may be mapped to the commonreference frame, first at the lane level, then at the junction level.For example, the same landmarks may be recognized multiple times bymultiple vehicles in multiple drives. The data regarding the samelandmarks received in different drives may be slightly different. Suchdata may be averaged and mapped to the same reference frame, such as theC0 reference frame. Additionally or alternatively, the variance of thedata of the same landmark received in multiple drives may be calculated.

In some embodiments, each lane of road segment 120 may be associatedwith a target trajectory and certain landmarks. The target trajectory ora plurality of such target trajectories may be included in theautonomous vehicle road navigation model, which may be used later byother autonomous vehicles travelling along the same road segment 1200.Landmarks identified by vehicles 1205, 1210, 1215, 1220, and 1225 whilethe vehicles travel along road segment 1200 may be recorded inassociation with the target trajectory. The data of the targettrajectories and landmarks may be continuously or periodically updatedwith new data received from other vehicles in subsequent drives.

For localization of an autonomous vehicle, the disclosed systems andmethods may use an Extended Kalman Filter. The location of the vehiclemay be determined based on three dimensional position data and/or threedimensional orientation data, prediction of future location ahead ofvehicle's current location by integration of ego motion. Thelocalization of vehicle may be corrected or adjusted by imageobservations of landmarks. For example, when vehicle detects a landmarkwithin an image captured by the camera, the landmark may be compared toa known landmark stored within the road model or sparse map 800. Theknown landmark may have a known location (e.g., GPS data) along a targettrajectory stored in the road model and/or sparse map 800. Based on thecurrent speed and images of the landmark, the distance from the vehicleto the landmark may be estimated. The location of the vehicle along atarget trajectory may be adjusted based on the distance to the landmarkand the landmark's known location (stored in the road model or sparsemap 800). The landmark's position/location data (e.g., mean values frommultiple drives) stored in the road model and/or sparse map 800 may bepresumed to be accurate.

In some embodiments, the disclosed system may form a closed loopsubsystem, in which estimation of the vehicle six degrees of freedomlocation (e.g., three dimensional position data plus three dimensionalorientation data) may be used for navigating (e.g., steering the wheelof) the autonomous vehicle to reach a desired point (e.g., 1.3 secondahead in the stored). In turn, data measured from the steering andactual navigation may be used to estimate the six degrees of freedomlocation.

In some embodiments, poles along a road, such as lampposts and power orcable line poles may be used as landmarks for localizing the vehicles.Other landmarks such as traffic signs, traffic lights, arrows on theroad, stop lines, as well as static features or signatures of an objectalong the road segment may also be used as landmarks for localizing thevehicle. When poles are used for localization, the x observation of thepoles (i.e., the viewing angle from the vehicle) may be used, ratherthan the y observation (i.e., the distance to the pole) since thebottoms of the poles may be occluded and sometimes they are not on theroad plane.

FIG. 23 illustrates a navigation system for a vehicle, which may be usedfor autonomous navigation using a crowdsourced sparse map. Forillustration, the vehicle is referenced as vehicle 1205. The vehicleshown in FIG. 23 may be any other vehicle disclosed herein, including,for example, vehicles 1210, 1215, 1220, and 1225, as well as vehicle 200shown in other embodiments. As shown in FIG. 12, vehicle 1205 maycommunicate with server 1230. Vehicle 1205 may include an image capturedevice 122 (e.g., camera 122). Vehicle 1205 may include a navigationsystem 2300 configured for providing navigation guidance for vehicle1205 to travel on a road (e.g., road segment 1200). Vehicle 1205 mayalso include other sensors, such as a speed sensor 2320 and anaccelerometer 2325. Speed sensor 2320 may be configured to detect thespeed of vehicle 1205. Accelerometer 2325 may be configured to detect anacceleration or deceleration of vehicle 1205. Vehicle 1205 shown in FIG.23 may be an autonomous vehicle, and the navigation system 2300 may beused for providing navigation guidance for autonomous driving.Alternatively, vehicle 1205 may also be a non-autonomous,human-controlled vehicle, and navigation system 2300 may still be usedfor providing navigation guidance.

Navigation system 2300 may include a communication unit 2305 configuredto communicate with server 1230 through communication path 1235.Navigation system 2300 may also include a GPS unit 2310 configured toreceive and process GPS signals. Navigation system 2300 may furtherinclude at least one processor 2315 configured to process data, such asGPS signals, map data from sparse map 800 (which may be stored on astorage device provided onboard vehicle 1205 and/or received from server1230), road geometry sensed by a road profile sensor 2330, imagescaptured by camera 122, and/or autonomous vehicle road navigation modelreceived from server 1230. The road profile sensor 2330 may includedifferent types of devices for measuring different types of roadprofile, such as road surface roughness, road width, road elevation,road curvature, etc. For example, the road profile sensor 2330 mayinclude a device that measures the motion of a suspension of vehicle2305 to derive the road roughness profile. In some embodiments, the roadprofile sensor 2330 may include radar sensors to measure the distancefrom vehicle 1205 to road sides (e.g., barrier on the road sides),thereby measuring the width of the road. In some embodiments, the roadprofile sensor 2330 may include a device configured for measuring the upand down elevation of the road. In some embodiment, the road profilesensor 2330 may include a device configured to measure the roadcurvature. For example, a camera (e.g., camera 122 or another camera)may be used to capture images of the road showing road curvatures.Vehicle 1205 may use such images to detect road curvatures.

The at least one processor 2315 may be programmed to receive, fromcamera 122, at least one environmental image associated with vehicle1205. The at least one processor 2315 may analyze the at least oneenvironmental image to determine navigation information related to thevehicle 1205. The navigation information may include a trajectoryrelated to the travel of vehicle 1205 along road segment 1200. The atleast one processor 2315 may determine the trajectory based on motionsof camera 122 (and hence the vehicle), such as three dimensionaltranslation and three dimensional rotational motions. In someembodiments, the at least one processor 2315 may determine thetranslation and rotational motions of camera 122 based on analysis of aplurality of images acquired by camera 122. In some embodiments, thenavigation information may include lane assignment information (e.g., inwhich lane vehicle 1205 is travelling along road segment 1200). Thenavigation information transmitted from vehicle 1205 to server 1230 maybe used by server 1230 to generate and/or update an autonomous vehicleroad navigation model, which may be transmitted back from server 1230 tovehicle 1205 for providing autonomous navigation guidance for vehicle1205.

The at least one processor 2315 may also be programmed to transmit thenavigation information from vehicle 1205 to server 1230. In someembodiments, the navigation information may be transmitted to server1230 along with road information. The road location information mayinclude at least one of the GPS signal received by the GPS unit 2310,landmark information, road geometry, lane information, etc. The at leastone processor 2315 may receive, from server 1230, the autonomous vehicleroad navigation model or a portion of the model. The autonomous vehicleroad navigation model received from server 1230 may include at least oneupdate based on the navigation information transmitted from vehicle 1205to server 1230. The portion of the model transmitted from server 1230 tovehicle 1205 may include an updated portion of the model. The at leastone processor 2315 may cause at least one navigational maneuver (e.g.,steering such as making a turn, braking, accelerating, passing anothervehicle, etc.) by vehicle 1205 based on the received autonomous vehicleroad navigation model or the updated portion of the model.

The at least one processor 2315 may be configured to communicate withvarious sensors and components included in vehicle 1205, includingcommunication unit 1705, GPS unit 2315, camera 122, speed sensor 2320,accelerometer 2325, and road profile sensor 2330. The at least oneprocessor 2315 may collect information or data from various sensors andcomponents, and transmit the information or data to server 1230 throughcommunication unit 2305. Alternatively or additionally, various sensorsor components of vehicle 1205 may also communicate with server 1230 andtransmit data or information collected by the sensors or components toserver 1230.

In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225 maycommunicate with each other, and may share navigation information witheach other, such that at least one of the vehicles 1205, 1210, 1215,1220, and 1225 may generate the autonomous vehicle road navigation modelusing crowdsourcing, e.g., based on information shared by othervehicles. In some embodiments, vehicles 1205, 1210, 1215, 1220, and 1225may share navigation information with each other and each vehicle mayupdate its own the autonomous vehicle road navigation model provided inthe vehicle. In some embodiments, at least one of the vehicles 1205,1210, 1215, 1220, and 1225 (e.g., vehicle 1205) may function as a hubvehicle. The at least one processor 2315 of the hub vehicle (e.g.,vehicle 1205) may perform some or all of the functions performed byserver 1230. For example, the at least one processor 2315 of the hubvehicle may communicate with other vehicles and receive navigationinformation from other vehicles. The at least one processor 2315 of thehub vehicle may generate the autonomous vehicle road navigation model oran update to the model based on the shared information received fromother vehicles. The at least one processor 2315 of the hub vehicle maytransmit the autonomous vehicle road navigation model or the update tothe model to other vehicles for providing autonomous navigationguidance.

FIG. 24 is a flowchart showing an exemplary process 2400 for generatinga road navigation model for use in autonomous vehicle navigation.Process 2400 may be performed by server 1230 or processor 2315 includedin a hub vehicle. In some embodiments, process 2400 may be used foraggregating vehicle navigation information to provide an autonomousvehicle road navigation model or to update the model.

Process 2400 may include receiving, by a server, navigation informationfrom a plurality of vehicles (step 2405). For example, server 1230 mayreceive the navigation information from vehicles 1205, 1210, 1215, 1220,and 1225. The navigation information from the plurality of vehicles maybe associated with a common road segment (e.g., road segment 1200) alongwhich the plurality of vehicles, e.g., 1205, 1210, 1215, 1220, and 1225,travel.

Process 2400 may further include storing, by the server, the navigationinformation associated with the common road segment (step 2410). Forexample, server 1230 may store the navigation information in storagedevice 2010 and/or memory 2015.

Process 2400 may further include generating, by the server, at least aportion of an autonomous vehicle road navigation model for the commonroad segment based on the navigation information from the plurality ofvehicles (step 2415). The autonomous vehicle road navigation model forthe common road segment may include at least one line representation ofa road surface feature extending along the common road segment, and eachline representation may represent a path along the common road segmentsubstantially corresponding with the road surface feature. For example,the road surface feature may include a road edge or a lane marking.Moreover, the road surface feature may be identified through imageanalysis of a plurality of images acquired as the plurality of vehiclestraverse the common road segment. For example, server 1230 may generateat least a portion of the autonomous vehicle road navigation model forcommon road segment 1200 based on the navigation information receivedfrom vehicles 1205, 1210, 1215, 1220, and 1225 that travel on the commonroad segment 1200.

In some embodiments, the autonomous vehicle road navigation model may beconfigured to be superimposed over a map, an image, or a satelliteimage. For example, the model may be superimposed over a map or imageprovided by a conventional navigation service such as Google® Maps,Waze, or the like.

In some embodiments, generating at least a portion of the autonomousvehicle road navigation model may include identifying, based on imageanalysis of the plurality of images, a plurality of landmarks associatedwith the common road segment. In certain aspects, this analysis mayinclude accepting potential landmarks when a ratio of images in whichthe landmark does appear to images in which the landmark does not appearexceeds a threshold and/or rejecting potential landmarks when a ratio ofimages in which the landmark does not appear to images in which thelandmark does appear exceeds a threshold. For example, if a potentiallandmark appears in data from vehicle 1210 but not in data from vehicles1205, 1215, 1220, and 1225, the system may determine that a ratio of 1:5is below the threshold for accepting the potential landmark. By way offurther example, if a potential landmark appears in data from vehicles1205, 1215, 1220, and 1225 but not in data from vehicle 1210, the systemmay determine that a ratio of 4:5 is above the threshold for acceptingthe potential landmark.

Process 2400 may further include distributing, by the server, theautonomous vehicle road navigation model to one or more autonomousvehicles for use in autonomously navigating the one or more autonomousvehicles along the common road segment (step 2420). For example, server1230 may distribute the autonomous vehicle road navigation model or aportion (e.g., an update) of the model to vehicles 1205, 1210, 1215,1220, and 1225, or any other vehicles later travel on road segment 1200for use in autonomously navigating the vehicles along road segment 1200.

Process 2400 may include additional operations or steps. For example,generating the autonomous vehicle road navigation model may includeclustering vehicle trajectories received from vehicles 1205, 1210, 1215,1220, and 1225 along road segment 1200 into a plurality of clustersand/or aligning data received from vehicles 1205, 1210, 1215, 1220, and1225, as discussed in further detail below with respect to FIG. 29.Process 2400 may include determining a target trajectory along commonroad segment 1200 by averaging the clustered vehicle trajectories ineach cluster. Process 2400 may also include associating the targettrajectory with a single lane of common road segment 1200. Process 2400may include determining a three dimensional spline to represent thetarget trajectory in the autonomous vehicle road navigation model.

Using Crowdsourced Sparse Maps for Navigation

As discussed above, server 1230 may distribute a generated roadnavigation model to one or more vehicles. The road navigation model maybe included in a sparse map, as described in detail above. Consistentwith embodiments of the present disclosure, one or more vehicles may beconfigured to use the distributed sparse map for autonomous navigation.

FIG. 25 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 25, memory 140 may store a sparse map module 2502, animage analysis module 2504, a road surface feature module 2506, and anavigational response module 2508. The disclosed embodiments are notlimited to any particular configuration of memory 140. Further,applications processor 180 and/or image processor 190 may execute theinstructions stored in any of modules 2502, 2504, 2506, and 2508included in memory 140. One of skill in the art will understand thatreferences in the following discussions to processing unit 110 may referto applications processor 180 and image processor 190 individually orcollectively. Accordingly, steps of any of the following processes maybe performed by one or more processing devices.

In one embodiment, sparse map module 2502 may store instructions which,when executed by processing unit 110, receive (and, in some embodiments,store) a sparse map distributed by server 1230. Sparse map module 2502may receive an entire sparse map in one communication or may receive asub-portion of a sparse map, the sub-portion corresponding to an area inwhich the vehicle is operating.

In one embodiment, image analysis module 2504 may store instructions(such as computer vision software) which, when executed by processingunit 110, performs analysis of one or more images acquired by one ofimage capture devices 122, 124, and 126. As described in further detailbelow, image analysis module 2504 may analyze the one or more images todetermine a current position of the vehicle.

In one embodiment, road surface feature module 2506 may storeinstructions which, when executed by processing unit 110, identifies aroad surface feature in the sparse map received by sparse map module2502 and/or in the one or more images acquired by one of image capturedevices 122, 124, and 126.

In one embodiment, navigational response module 2508 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of sparse map module 2502,image analysis module 2504, and/or road surface feature module 2506.

Furthermore, any of the modules (e.g., modules 2502, 2504, and 2506)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 26 is a flowchart showing an exemplary process 2600 forautonomously navigating a vehicle along a road segment. Process 2600 maybe performed by processor 2315 included in navigation system 2300.

Process 2600 may include receiving a sparse map model (step 2605). Forexample, processor 2315 may receive the sparse map from server 1230. Insome embodiments, the sparse map model may include at least one linerepresentation of a road surface feature extending along the roadsegment, and each line representation may represent a path along theroad segment substantially corresponding with the road surface feature.For example, the road feature may include a road edge or a lane marking.

Process 2600 may further include receiving, from a camera, at least oneimage representative of an environment of the vehicle (step 2610). Forexample, processor 2315 may receive, from camera 122, the at least oneimage. Camera 122 may capture one or more images of the environmentsurrounding vehicle 1205 as vehicle 1205 travels along road segment1200.

Process 2600 may also include analyzing the sparse map model and the atleast one image received from the camera (step 2615). For example,analysis of the sparse map model and the at least one image receivedfrom the camera may include determining a current position of thevehicle relative to a longitudinal position along the at least one linerepresentation of a road surface feature extending along the roadsegment. In some embodiments, this determination may be based onidentification of at least one recognized landmark in the at least oneimage. In some embodiments, process 2600 may further include determiningan estimated offset based on an expected position of the vehiclerelative to the longitudinal position and the current position of thevehicle relative to the longitudinal position.

Process 2600 may further include determining an autonomous navigationalresponse for the vehicle based on the analysis of the sparse map modeland the at least one image received from the camera (step 2620). Inembodiments in which processor 2315 determines an estimated offset, theautonomous navigational response may be further based on the estimatedoffset. For example, if processor 2315 determines that the vehicle isoffset from the at least one line representation by 1 m to the left,processor 2315 may cause the vehicle to shift towards the right (e.g.,by changing an orientation of the wheels). By way of further example, ifprocessor 2315 determines that an identified landmark is offset from anexpect position, processor 2315 may cause the vehicle to shift so as tomove the identified landmark towards its expected position. Accordingly,in some embodiments, process 2600 may further include adjusting asteering system of the vehicle based on the autonomous navigationalresponse.

Aligning Crowdsourced Map Data

As discussed above, generation of a crowdsourced sparse map may use datafrom a plurality of drives along a common road segment. This data may bealigned in order to generate a coherent sparse map. As discussed abovewith respect to FIG. 14, generating a map skeleton may be insufficientto construct splines for use in navigation. Thus, embodiments of thepresent disclosure may allow for aligning data crowdsourced from aplurality of drives.

FIG. 27 illustrates a block diagram of memory 2015, which may storecomputer code or instructions for performing one or more operations forgenerating a road navigation model for use in autonomous vehiclenavigation. As shown in FIG. 21, memory 2015 may store one or moremodules for performing the operations for processing vehicle navigationinformation. For example, memory 2015 may include a drive data receivingmodule 2705 and a longitudinal alignment module 2710. Processor 2020 mayexecute the instructions stored in any of modules 2705 and 2710 includedin memory 2015.

Drive data receiving module 2705 may store instructions which, whenexecuted by processor 2020, may control communication device 2005 toreceive drive data from one or more vehicles (e.g., 1205, 1210, 1215,1220, and 1225).

Longitudinal alignment module 2710 may store instructions which, whenexecuted by processor 2020, align the data received using drive datareceiving module 2705 when the data is related to common road segment(e.g., road segment 1200) based on navigation information received fromvehicles 1205, 1210, 1215, 1220, and 1225. For example, longitudinalalignment module 2710 may align the data along patches, which may allowfor easier optimization of the error correction that alignment entails.In some embodiments, longitudinal alignment module 2710 may furtherscore each patch alignment with a confidence score.

FIG. 28A illustrates an example of raw location data from four differentdrives. In the example of FIG. 28A, the raw data from the first drive isdepicted as a series of stars, the raw data from the second drive isdepicted as a series of filled-in squares, the raw data from third firstdrive is depicted as a series of open squares, and the raw data from thefourth drive is depicted as a series of open circles. As one skilled inthe art would recognize, the shapes are merely illustrative of the dataitself, which may be stored as a series of coordinates, whether local orglobal.

As may be seen in FIG. 28A, the drives may occur in different lanesalong the same road (represented by line 1200). Furthermore, FIG. 28Adepicts that the drive data may include variances due to errors inlocational measurements (e.g., GPS) and may have missing data points dueto system errors. Finally, FIG. 28A also depicts that each drive maystart and end at a different point within a segment along the road.

FIG. 28B illustrates another example of raw location data from fivedifferent drives. In the example of FIG. 28B, the raw data from thefirst drive is depicted as a series of filled-in squares, the raw datafrom the second drive is depicted as a series of open squares, the rawdata from third first drive is depicted as a series of open circles, theraw data from the fourth drive is depicted as a series of stars, and theraw data from the fifth drive is depicted as a series of triangles. Asone skilled in the art would recognize, the shapes are merelyillustrative of the data itself, which may be stored as a series ofcoordinates, whether local or global.

FIG. 28B illustrates similar properties of the drive data as FIG. 28A.FIG. 28B further depicts that an intersection may be detected bytracking the movement of the fifth drive away from the others. Forexample, the example data in FIG. 28B may suggest that an exit ramp ispresent on the right side of the road (represented by line 1200). FIG.28B also depicts that added lanes may be detected if data begins on anew portion of the road. For example, the fourth drive in the exampledata in FIG. 28B may suggest that a fourth lane is added to the roadshortly after the detected exit ramp.

FIG. 28C illustrates an example of raw location data with targettrajectories therefrom. For example, the first drive data (representedby triangles) and the second drive data (represented by open squares)have an associated target trajectory 2810. Similarly, the third drivedata (represented by open circles) has an associated target trajectory2820, and the fourth drive data (represented by filled-in squares) hasan associated target trajectory 2830.

In some embodiments, drive data may be reconstructed such that onetarget trajectory is associated with each lane of travel, as depicted inFIG. 28C. Such target trajectories may be generated from one or moresimple smooth line models if proper alignment of the patches of thedrive data is performed. Process 2900, discussed below, is one exampleof proper alignment of patches.

FIG. 29 is a flowchart showing an exemplary process 2900 for determininga line representation of a road surface feature extending along a roadsegment. The line representation of the road surface feature may beconfigured for use in autonomous vehicle navigation, e.g., using process2600 of FIG. 26 above. Process 2900 may be performed by server 1230 orprocessor 2315 included in a hub vehicle.

Process 2900 may include receiving, by a server, a first set of drivedata including position information associated with the road surfacefeature (step 2905). The position information may be determined based onanalysis of images of the road segment, and the road surface feature mayinclude a road edge or a lane marking.

Process 2900 may further include receiving, by a server, a second set ofdrive data including position information associated with the roadsurface feature (step 2910). As with step 2905, the position informationmay be determined based on analysis of images of the road segment, andthe road surface feature may include a road edge or a lane marking.Steps 2905 and 2910 may be performed at concurrent times, or there maybe a lapse of time between step 2905 and step 2910, depending on whenthe first set of drive data and the second set of drive data arecollected.

Process 2900 may also include segmenting the first set of drive datainto first drive patches and segmenting the second set of drive datainto second drive patches (step 2915). A patch may be defined by a datasize or by a drive length. The size or length that defines a patch maybe predefined to one or more values or may be updated with a neuralnetwork or other machine technology. For example, the length of a patchmay be preset to always be 1 km, or it may be preset to be 1 km whentraveling along a road at greater than 30 km/hr and to be 0.8 km whentraveling along a road at less than 30 km/hr. Alternatively orconcurrently, machine learning analysis may optimize the size or lengthof patches based on a number of dependent variables such as drivingconditions, driving speed, and the like. Still further, in someembodiments, a path may be defined by a data size and a drive length.

In some embodiments, the first set of data and the second set of datamay include position information and may be associated with a pluralityof landmarks. In such embodiments, process 2900 may further includedetermining whether to accept or rejection landmarks within the set s ofdata based on one or more thresholds, as described above with respect toFIG. 19.

Process 2900 may include longitudinally aligning the first set of drivedata with the second set of drive data within corresponding patches(step 2920). For example, longitudinal alignment may include selectingeither the first set of data or the second set of data as the referencedata set and then shifting and/or elastically stretching the other setof data to align patches within the sets. In some embodiments, aligningthe sets of data may further include aligning GPS data included in bothsets and associated with the patches. For example, the connectionsbetween patches with the sets may be adjusted to align more closely withGPS data. In such embodiments, however, the adjustment must be limitedto prevent the limitations of GPS data from corrupting the alignment.For example, GPS data is not three-dimensional and, therefore, maycreate unnatural twists and slopes when projected onto athree-dimensional representation of the road.

Process 2900 may further include determining the line representation ofthe road surface feature based on the longitudinally aligned first andsecond drive data in the first and second draft patches (step 2925). Forexample, the line representation may be constructed using a smooth linemodel on the aligned data. In some embodiments, determining the linerepresentation may include an alignment of the line representation withglobal coordinates based on GPS data acquired as part of at least one ofthe first set of drive data or the second set of drive data. Forexample, the first set of data and/or the second set of data may berepresented in local coordinates; however, the use of a smooth linemodel requires both sets to have the same coordinate axes. Accordingly,in certain aspects, the first set of data and/or second set of data maybe adjusted to have the same coordinate axes as each other.

In some embodiments, determining the line representation may includedetermining and applying a set of average transformations. For example,each of the average transformations may be based on transformationsdetermined that link data from the first set of drive data acrosssequential patches and the link data from the second set of drive dataacross sequential patches.

Process 2900 may include additional operations or steps. For example,process 2900 may further include overlaying the line representation ofthe road surface feature on at least one geographical image. Forexample, the geographical image may be a satellite image. By way offurther example, process 2900 may further include filtering out landmarkinformation and/or drive data that appears erroneous based on thedetermined line representation and longitudinal alignment. Suchfiltering may be similar in concept to the rejection of possiblelandmarks based on one or more thresholds, as described above withrespect to FIG. 19.

Crowdsourcing Road Surface Information

In addition to crowdsourcing landmarks and line representations in orderto generate a sparse map, the disclosed systems and methods maycrowdsource road surface information as well. Accordingly, roadconditions may be stored along with and/or within a sparse map used fornavigation of an autonomous vehicle.

FIG. 30 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 30, memory 140 may store an image receiving module3002, a road surface feature module 3004, a location determinationmodule 3006, and a navigational response module 3008. The disclosedembodiments are not limited to any particular configuration of memory140. Further, applications processor 180 and/or image processor 190 mayexecute the instructions stored in any of modules 3002, 3004, 3006, and3008 included in memory 140. One of skill in the art will understandthat references in the following discussions to processing unit 110 mayrefer to applications processor 180 and image processor 190 individuallyor collectively. Accordingly, steps of any of the following processesmay be performed by one or more processing devices.

In one embodiment, image receiving module 3002 may store instructionswhich, when executed by processing unit 110, receive (and, in someembodiments, store) images acquired by one of image capture devices 122,124, and 126.

In one embodiment, road surface feature module 3004 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs analysis of one or more images acquired byone of image capture devices 122, 124, and 126 and identifies a roadsurface feature. For example, the road surface feature may include aroad edge or a lane marking.

In one embodiment, location determination module 3006 may storeinstructions (such as GPS software or visual odometry software) which,when executed by processing unit 110, receives location informationrelating to the vehicle. For example, location determination module 3006may receive GPS data and/or ego-motion data including the position ofthe vehicle. In some embodiments, location determination module 3006 maycalculate one or more locations using received information. For example,location determination module 3006 may receive one or more imagesacquired by one of image capture devices 122, 124, and 126 and determinea location of the vehicle using analysis of the images.

In one embodiment, navigational response module 3008 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of image receiving module3002, road surface feature module 3004, and/or location determinationmodule 3006.

Furthermore, any of the modules (e.g., modules 3002, 3004, and 3006)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 31 is a flowchart showing an exemplary process 3100 for collectingroad surface information for a road segment. Process 3100 may beperformed by processor 2315 included in navigation system 2300.

Process 3100 may include receiving, from a camera, at least one imagerepresentative of a portion of the road segment (step 3105). Forexample, processor 2315 may receive, from camera 122, the at least oneimage. Camera 122 may capture one or more images of the environmentsurrounding vehicle 1205 as vehicle 1205 travels along road segment1200.

Process 3100 may further include identifying in the at least one imageat least one road surface feature along the portion of the road segment(step 3010). For example, the at least one road surface feature mayinclude a road edge or may include a lane marking.

Process 3100 may also include determining a plurality of locationsassociated with the road surface feature according to a local coordinatesystem of the vehicle (step 3115). For example, processor 2315 may useego-motion data and/or GPS data to determine the plurality of locations.

Process 3100 may further include transmitting the determined pluralityof locations from the vehicle to a server (step 3120). For example, thedetermined locations may be configured to enable determination by theserver of a line representation of the road surface feature extendingalong the road segment, as described above with respect to FIG. 29. Insome embodiments, the line representation may represent a path along theroad segment substantially corresponding with the road surface feature.

Process 3100 may include additional operations or steps. For example,process 3100 may further include receiving, from the server, the linerepresentation. In this example, processor 2315 may receive the linerepresentation as a portion of a sparse map received, for example,according to process 2400 of FIG. 24 and/or process 2600 of FIG. 26. Byway of further example, process 2900 may further include overlaying theline representation of the road surface feature on at least onegeographical image. For example, the geographical image may be asatellite image.

As explained above with respect to crowdsourcing of landmarks, in someembodiments, the server may implement selection criteria for decidingwhether to accept or reject possible road surface features received fromthe vehicles. For example, the server may accept road surface featureswhen a ratio of location sets in which the road surface feature doesappear to location sets in which the road surface feature does notappear exceeds a threshold and/or rejecting potential road surfacefeatures when a ratio of location sets in which the road surface featuredoes not appear to location sets in which the road surface feature doesappear exceeds a threshold.

Vehicle Localization

In some embodiments, the disclosed systems and methods may use a sparsemap for autonomous vehicle navigation. In particular, the sparse map maybe for autonomous vehicle navigation along a road segment. For example,the sparse map may provide sufficient information for navigating anautonomous vehicle without storing and/or updating a large quantity ofdata. As discussed below in further detail, an autonomous vehicle mayuse the sparse map to navigate one or more roads based on one or morestored trajectories.

Autonomous Vehicle Lane Localization Using Lane Markings

As explained above, a self-driving vehicle may navigate based on deadreckoning between landmarks. However, errors may accumulate duringnavigation by dead reckoning, and thus over time the positiondeterminations relative to the target trajectory may become increasinglyless accurate. As explained below, lane markings may be used forlocalization of the vehicle during landmark spacings, which may minimizeaccumulation of errors during navigation by dead reckoning.

For example, a spline may be represented as shown in Equation 1 below:

$\begin{matrix}{{B(u)} = {\sum\limits_{k}\;{{b_{k}(u)}P^{(k)}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In the example of Equation 1, B(u) is the curve representing the spline,b_(k)(u) are the basis functions, and P^((k)) represents a controlpoint. The control point may be transformed to local coordinatesaccording, for example, to Equation 2 below:P _(l) ^((k)) =R ^(T)(P ^((k)) −T)  Equation 2

In the example of Equation 2, P_(l) ^((k)) represents the control pointP^((k)) transformed to local coordinates, R is the rotation matrix thatmay, for example, be inferred from the vehicle's heading, R^(T)represents the transpose of the rotation matrix, and T represents thelocation of the vehicle.

In some embodiments, the local curve representing the path of thevehicle may be determined using Equation 3 below:H _(i)(u)=y _(i) B _(lz)(u)−fB _(ly)(u)=0  Equation 3

In the example of Equation 3, f is the focal length of the camera andB_(lz), B_(ly), and B_(lx) represent the components of curve B in localcoordinates. The derivation of H may be represented by Equation 4 below:H′(u)=y _(i) B′ _(lz)(u)−fB′ _(ly)(u)  Equation 4

Based on Equation 1, B′_(l) in Equation 4 may be further represented byEquation 5 below:

$\begin{matrix}{{B_{l}^{\prime}(u)} = {\sum\limits_{k}{{b_{k}^{\prime}(u)}P_{l}^{(k)}}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

Equation 5 may, for example, be solved by a Newton-Raphson-based solver.In certain aspects, the solver may be run for five or fewer steps. Tosolve for x, in some embodiments, one may use Equation 6 below:

$\begin{matrix}{x = {f\frac{B_{lx}\left( u_{i} \right)}{B_{lz}\left( u_{i} \right)}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

In some embodiments, the derivatives of a trajectory may be used. Forexample, the derivatives may be given by Equation 7 below:

$\begin{matrix}{\frac{dx}{{dX}_{j}} = {{f\frac{1}{B_{lz}\left( u_{i} \right)}\frac{{dB}_{lx}\left( u_{i} \right)}{{dX}_{j}}} + {f\frac{B_{lx}\left( u_{i} \right)}{{B_{lz}\left( u_{i} \right)}^{2}}\frac{{dB}_{lz}\left( u_{i} \right)}{{dX}_{j}}}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

In the example of Equation 7, X_(j) may represent state components, forexample, for the location of the vehicle. In certain aspects, j mayrepresent an integer between one and six.

To solve Equation 7, in some embodiments, one may use Equation 8 below:

$\begin{matrix}{\frac{{dB}_{lx}\left( u_{i} \right)}{{dX}_{j}} = {{\sum\limits_{k}{\frac{d}{{dX}_{j}}\left( {{b_{k}\left( u_{i} \right)}P_{lx}^{(k)}} \right)}} = {\sum\limits_{k}\left( {{{b_{k}^{\prime}\left( u_{i} \right)}\frac{{du}_{i}}{{dX}_{j}}} + {{b_{k}\left( u_{i} \right)}\frac{{dP}_{lx}^{(k)}}{{dX}_{j}}}} \right)}}} & {{Equation}\mspace{14mu} 8}\end{matrix}$

To solve Equation 8, in some embodiments, one may use implicitdifferentiation to obtain Equations 9 and 10 below:

$\begin{matrix}{{\frac{\partial{B_{lz}\left( u_{i} \right)}}{\partial X_{j}} = {\sum\limits_{k}\;{{b_{k}\left( u_{i} \right)}\frac{{dP}_{lz}^{(k)}}{{dX}_{j}}}}}{\frac{\partial{B_{ly}\left( u_{i} \right)}}{\partial X_{j}} = {\sum\limits_{k}\;{{b_{k}\left( u_{i} \right)}\frac{{dP}_{ly}^{(k)}}{{dX}_{j}}}}}} & {{Equations}\mspace{14mu} 9\mspace{14mu}{and}\mspace{14mu} 10}\end{matrix}$

Using Equations 9 and 10, the derivatives of the trajectory may beobtained. In some embodiments, then, the Extended Kalman Filter may beused to localize lane measurements. By localizing lane measurements, asexplained above, lane markings may be used to minimize accumulation oferrors during navigation by dead reckoning. The use of lane markings isdescribed in further detail below with respect to FIGS. 32-35.

FIG. 32 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 32, memory 140 may store a position determinationmodule 3202, an image analysis module 3204, a distance determinationmodule 3206, and an offset determination module 3208. The disclosedembodiments are not limited to any particular configuration of memory140. Further, applications processor 180 and/or image processor 190 mayexecute the instructions stored in any of modules 3202, 3204, 3206, and3208 included in memory 140. One of skill in the art will understandthat references in the following discussions to processing unit 110 mayrefer to applications processor 180 and image processor 190 individuallyor collectively. Accordingly, steps of any of the following processesmay be performed by one or more processing devices.

In one embodiment, position determination module 3202 may storeinstructions (such as GPS software or visual odometry software) which,when executed by processing unit 110, receives location informationrelating to the vehicle. For example, position determination module 3202may receive GPS data and/or ego-motion data including the position ofthe vehicle. In some embodiments, position determination module 3202 maycalculate one or more locations using received information. For example,position determination module 3202 may receive one or more imagesacquired by one of image capture devices 122, 124, and 126 and determinea location of the vehicle using analysis of the images.

Position determination module 3202 may also use other navigationalsensors to determine the position of the vehicle. For example, a speedsensor or accelerometer may send information to position determinationmodule 3202 for use in calculating the position of the vehicle.

In one embodiment, image analysis module 3204 may store instructions(such as computer vision software) which, when executed by processingunit 110, performs analysis of one or more images acquired by one ofimage capture devices 122, 124, and 126. As described in further detailbelow, image analysis module 3204 may analyze the one or more images toidentify at least one lane marking.

In one embodiment, distance determination module 3206 may storeinstructions which, when executed by processing unit 110, performsanalysis of one or more images acquired by one of image capture devices122, 124, and 126 and determines a distance from the vehicle to a lanemarking identified by image analysis module 3204.

In one embodiment, offset determination module 3208 may store softwareexecutable by processing unit 110 to determine an estimated offset ofthe vehicle from a road model trajectory. For example, offsetdetermination module 3208 may use a distance determined by distancedetermination module 3206 to calculate the estimated offset. A desirednavigational response based on data derived from execution of positiondetermination module 3202, image analysis module 3204, distancedetermination module 3206, and/or offset determination module 3208 maythen be determined.

Furthermore, any of the modules (e.g., modules 3202, 3204, and 3206)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 33A illustrates an example of a vehicle navigating by deadreckoning without using lane markings. In the example of FIG. 33A, thevehicle is navigating along a trajectory 3310 but does not use lanemarkings (e.g., markings 3320A or 3320B) for navigating.

FIG. 33B shows the example of FIG. 33A after 350 m. As depicted in FIG.33B, the trajectory 3310 of the vehicle does not fully align with thelane markings (e.g., markings 3320A or 3320B) due to dead reckoningerror accumulation.

FIG. 33C shows the example of FIGS. 33A and 33B after 1 km. As depictedin FIG. 33C, the expected position 3330A of a landmark does not alignwith the actual position 3330B of the landmark. Although the vehicle maynow use the landmark to correct the dead reckoning errors thataccumulated over the course of 1 km, systems and methods of the presentdisclosure may allow for using lane markings to minimize dead reckoningerror accumulation between landmarks.

FIG. 34A illustrates an example of a vehicle navigating by deadreckoning with lane markings. In the example of FIG. 34A, the vehicle isnavigating along a trajectory 3410 and also identified lane markings(e.g., markings 3420A and 3420B) to localize and use for navigating.

FIG. 34B shows the example of FIG. 34A after 350 m. As depicted in FIG.34B, the trajectory 3410 of the vehicle substantially aligns with thelane markings (e.g., markings 3420A and 3420B) because the vehicle hasbeen correcting for dead reckoning error using the identified lanemarkings.

FIG. 34C shows the example of FIGS. 34A and 34B after 1 km. As depictedin FIG. 34C, the expected position 3430A of a landmark substantiallyaligns with the actual position 3430B of the landmark. Accordingly, uponencountering the landmark, the vehicle in FIG. 34C may undertake asubstantially smaller correction than the vehicle of FIG. 33C. Process3500 of FIG. 35, discussed below, is an exemplary process by which avehicle may use lane markings for navigation like in FIGS. 34A-34C.

FIG. 35 is a flowchart showing an exemplary process 3500 for correctinga position of a vehicle navigating a road segment. Process 3500 may beperformed by processor 2315 included in navigation system 2300.

Process 3500 may include determining, based on an output of at least onenavigational sensor, a measured position of the vehicle along apredetermined road model trajectory (step 3505). For example, thepredetermined road model trajectory may be associated with the roadsegment, and, in some embodiments, the predetermined road modeltrajectory may include a three-dimensional polynomial representation ofa target trajectory along the road segment. The at least onenavigational sensor may, for example, include a speed sensor or anaccelerometer.

Process 3500 may further include receiving, from an image capturedevice, at least one image representative of an environment of thevehicle (step 3510). For example, processor 2315 may receive, fromcamera 122, the at least one image. Camera 122 may capture one or moreimages of the environment surrounding vehicle 1205 as vehicle 1205travels along road segment 1200.

Process 3500 may also include analyzing the at least one image toidentify at least one lane marking. The at least one lane marking may beassociated with a lane of travel along the road segment. Process 3500may further include determining, based on the at least one image, adistance from the vehicle to the at least one lane marking. For example,a variety of known algorithms for calculating the distance of an objectin an image may be used.

Process 3500 may include determining an estimated offset of the vehiclefrom the predetermined road model trajectory based on the measuredposition of the vehicle and the determined distance. In someembodiments, determining the estimated offset may further includedetermining, based on the distance to the at least one lane marking,whether the vehicle is on a trajectory that will intersect the at leastone lane marking. Alternatively or concurrently, in some embodiments,determining the estimated offset may further include determining, basedon the distance to the at least one lane marking, whether the vehicle iswithin a predetermined threshold of the at least one lane marking.

Process 3500 may further include determining an autonomous steeringaction for the vehicle based on the estimated offset to correct theposition of the vehicle. For example, in embodiments where the estimatedoffset includes determining whether the vehicle is on a trajectory thatwill intersect the at least one lane marking and/or whether the vehicleis within a predetermined threshold of the at least one lane marking,processor 2315 may determine an autonomous steering action to preventthe intersection of the vehicle trajectory with the at least one lanemarking and/or to bring the distance between the vehicle and the lanemarking below the predetermined threshold.

In some embodiments, determining the autonomous steering action mayfurther include solving for at least one derivative of the predeterminedroad model trajectory. For example, the derivative of a trajectory maybe calculated using the Extended Kalman Filter, discussed above.

Process 3500 may include additional operations or steps. For example,process 3500 may further include adjusting a steering system of thevehicle based on the autonomous steering action.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, 4K Ultra HD Blu-ray,or other optical drive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A method of generating a road navigation modelfor use in autonomous vehicle navigation, the method comprising:receiving, by a server, navigation information from a plurality ofvehicles, wherein: the navigation information from the plurality ofvehicles is associated with a common road segment; the navigationinformation from the plurality of vehicles includes first positioninformation and second position information for a road surface featureextending along the common road segment; and the first positioninformation and the second position information have been determinedbased on analysis of a plurality of images acquired as the plurality ofvehicles traverse the common road segment; storing, by the server, thenavigation information associated with the common road segment;generating, by the server, at least a portion of an autonomous vehicleroad navigation model for the common road segment based on thenavigation information from the plurality of vehicles, the autonomousvehicle road navigation model for the common road segment including atleast one line representation of a road surface feature extending alongthe common road segment, wherein: each line representation represents apath along the common road segment substantially corresponding with theroad surface feature; each line representation is constructed byaligning the first position information and the second positioninformation; and the road surface feature is identified through theimage analysis of the plurality of images acquired as the plurality ofvehicles traverse the common road segment; and distributing, by theserver, the autonomous vehicle road navigation model to one or moreautonomous vehicles for use in autonomously navigating the one or moreautonomous vehicles along the common road segment.
 2. The method ofclaim 1, wherein the autonomous vehicle road navigation model isconfigured to be superimposed over a map, an image, or a satelliteimage.
 3. The method of claim 1, wherein the plurality of road featuresincludes road edges or lane markings.
 4. The method of claim 1, whereingenerating at least a portion of the autonomous vehicle road navigationmodel includes identifying, based on image analysis of the plurality ofimages, a plurality of landmarks associated with the common roadsegment.
 5. The method of claim 4, wherein generating at least a portionof the autonomous vehicle road navigation model further includesaccepting potential landmarks when a ratio of images in which thelandmark does appear to images in which the landmark does not appearexceeds a threshold.
 6. The method of claim 4, wherein generating atleast a portion of the autonomous vehicle road navigation model furtherincludes rejecting potential landmarks when a ratio of images in whichthe landmark does not appear to images in which the landmark does appearexceeds a threshold.
 7. A system for generating a road navigation modelfor use in autonomous vehicle navigation, the system comprising: atleast one network interface; at least one non-transitory storage medium;and at least one processing device, wherein the at least one processingdevice is configured to: receive, using the network interface,navigation information from a plurality of vehicles, wherein: thenavigation information from the plurality of vehicles is associated witha common road segment; the navigation information from the plurality ofvehicles includes first position information and second positioninformation for a road surface feature extending along the common roadsegment; and the first position information and the second positioninformation have been determined based on analysis of a plurality ofimages acquired as the plurality of vehicles traverse the common roadsegment; store, on the non-transitory storage medium, the navigationinformation associated with the common road segment; generate at least aportion of an autonomous vehicle road navigation model for the commonroad segment based on the navigation information from the plurality ofvehicles, the autonomous vehicle road navigation model for the commonroad segment including at least one line representation of a roadsurface feature extending along the common road segment, wherein: eachline representation represents a path along the common road segmentsubstantially corresponding with the road surface feature; each linerepresentation is constructed by aligning the first position informationand the second position information; and the road surface feature isidentified through image analysis of a plurality of images acquired asthe plurality of vehicles traverse the common road segment; anddistribute, using the network interface, the autonomous vehicle roadnavigation model to one or more autonomous vehicles for use inautonomously navigating the one or more autonomous vehicles along thecommon road segment.
 8. The system of claim 7, wherein the autonomousvehicle road navigation model is configured to be superimposed over amap, an image, or a satellite image.
 9. The system of claim 7, whereinthe plurality of road features includes road edges or lane markings. 10.The system of claim 7, wherein generating at least a portion of theautonomous vehicle road navigation model includes identifying, based onimage analysis of the plurality of images, a plurality of landmarksassociated with the common road segment.
 11. The system of claim 10,wherein generating at least a portion of the autonomous vehicle roadnavigation model further includes accepting potential landmarks when aratio of images in which the landmark does appear to images in which thelandmark does not appear exceeds a threshold.
 12. The system of claim10, wherein generating at least a portion of the autonomous vehicle roadnavigation model further includes rejecting potential landmarks when aratio of images in which the landmark does not appear to images in whichthe landmark does appear exceeds a threshold.
 13. A non-transitory,computer-readable medium storing instructions that, when executed by atleast one processing device, cause the server to: receive navigationinformation from a plurality of vehicles, wherein: the navigationinformation from the plurality of vehicles is associated with a commonroad segment; the navigation information from the plurality of vehiclesincludes first position information and second position information fora road surface feature extending along the common road segment; and thefirst position information and the second position information have beendetermined based on analysis of a plurality of images acquired as theplurality of vehicles traverse the common road segment; store thenavigation information associated with the common road segment; generateat least a portion of an autonomous vehicle road navigation model forthe common road segment based on the navigation information from theplurality of vehicles, the autonomous vehicle road navigation model forthe common road segment including at least one line representation of aroad surface feature extending along the common road segment, wherein:each line representation represents a path along the common road segmentsubstantially corresponding with the road surface feature; each linerepresentation is constructed by aligning the first position informationand the second position information; and the road surface feature isidentified through image analysis of a plurality of images acquired asthe plurality of vehicles traverse the common road segment; anddistribute the autonomous vehicle road navigation model to one or moreautonomous vehicles for use in autonomously navigating the one or moreautonomous vehicles along the common road segment.
 14. Thenon-transitory, computer-readable medium of claim 13, wherein theautonomous vehicle road navigation model is configured to besuperimposed over a map, an image, or a satellite image.
 15. Thenon-transitory, computer-readable medium of claim 13, wherein theplurality of road features includes road edges or lane markings.
 16. Thenon-transitory, computer-readable medium of claim 13, wherein theinstructions to generate at least a portion of the autonomous vehicleroad navigation model include instructions to identify, based on imageanalysis of the plurality of images, a plurality of landmarks associatedwith the common road segment.
 17. The non-transitory, computer-readablemedium of claim 16, wherein the instructions to generate at least aportion of the autonomous vehicle road navigation model further includeinstructions to accept potential landmarks when a ratio of images inwhich the landmark does appear to images in which the landmark does notappear exceeds a threshold.
 18. The non-transitory, computer-readablemedium of claim 16, wherein the instructions to generate at least aportion of the autonomous vehicle road navigation model further includeinstructions to reject potential landmarks when a ratio of images inwhich the landmark does not appear to images in which the landmark doesappear exceeds a threshold.
 19. The method of claim 1, wherein each linerepresentation comprises at least one of a spline, a polynomialrepresentation, or a curve.
 20. The method of claim 19, whereingenerating at least a portion of an autonomous vehicle road navigationmodel comprises applying a parabolic spline algorithm to the receivednavigation information to determine the at least one linerepresentation.
 21. The method of claim 19, wherein generating at leasta portion of an autonomous vehicle road navigation model comprisesdetermining coefficients from the received navigation information tostore as the at least one line representation.